{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Harry Potter Movie Review Analysis Weighted Title\n",
    "## By Ali Ho "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Packages and functions to be used\n",
    "import os \n",
    "\n",
    "#For regex \n",
    "import re\n",
    "\n",
    "#importing pandas \n",
    "import pandas as pd \n",
    "\n",
    "#To create a wordcloud/graphs\n",
    "import numpy as np \n",
    "from wordcloud import WordCloud, ImageColorGenerator\n",
    "from PIL import Image\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt \n",
    "from matplotlib import cm \n",
    "from colorspacious import cspace_converter\n",
    "import seaborn as sns\n",
    "#Allows for randomization, you can set a seed to have reproducable results\n",
    "import random\n",
    "\n",
    "#from PIL import Image\n",
    "from PIL import ImageFilter\n",
    "import numpy as np\n",
    "#Allows for several values for the same dictionary key \n",
    "import multidict\n",
    "\n",
    "#To get a count of words (used in the term_frequency)\n",
    "from collections import Counter \n",
    "\n",
    "#NLTK Packages \n",
    "#To process text using nltk (remove stopwords, lemmatize, tokenize...)\n",
    "from nltk.corpus import stopwords\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer \n",
    "#Porter stemmer \n",
    "from nltk.stem.porter import PorterStemmer \n",
    "\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer as SIA\n",
    "\n",
    "#To perform machine learning in Naive Bayes I need to import the following packages \n",
    "from sklearn.model_selection import train_test_split \n",
    "# To model the Gaussian Navie Bayes classifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "#Multinomrial classifier for naive bayes\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "#SVMs \n",
    "from sklearn.svm import SVC\n",
    "# To calculate the accuracy score of the model\n",
    "from sklearn.metrics import accuracy_score\n",
    "#confusion matrix \n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions I created that will be used in the document: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function 1: Creating a function to read in my files. This function will read in all the files in a specific directory. \n",
    "    #inputs: list of all of the file names to read \n",
    "    #        the path where the files are located \n",
    "    #outputs: a list, each element in the list is the content for each file that was read in. \n",
    "def reading_in_files(list_of_file_names, path):\n",
    "    empty_list = []\n",
    "    for file in list_of_file_names: \n",
    "        file = open(path+ \"\\\\\" + file)\n",
    "        data = file.read() \n",
    "        empty_list.append(data)\n",
    "        file.close()\n",
    "    return(empty_list)\n",
    "\n",
    "#Function 2: Creating a function to split the output from the just_compound_and_doc function. \n",
    "    #inputs: the list that is to be split,\n",
    "            #the item that we want to split on \n",
    "    #outputs: a list of split lists that when put into a df, will create columns based on where the list was split\n",
    "def list_split(list_to_be_split, item_to_split_on):\n",
    "    empty_list = []\n",
    "    for element in list_to_be_split: \n",
    "        empty_list.append(element.split(item_to_split_on))\n",
    "    return(empty_list)\n",
    "\n",
    "#Function 2: Vader (from NLTK) sentiment intensity score calculator. This function will calculate the polarity score \n",
    "#for each file. It will return a list of dictionaries. Each dictionary will contain the following: a compound score, \n",
    "#positive score, negative score, neutral score, and the opinion. \n",
    "    #inputs: a list of the content to be analyzed (this is what is returned in the reading_in_files function)\n",
    "    #Output: a list of dictionaries. Each dictionary will contain the following: a compound score, postive score, \n",
    "    #        negative score, neutral score, and the opinion \n",
    "def sentiment_intensity_score(sentiment_files): \n",
    "    sent_analyze = SIA()\n",
    "    results = []\n",
    "    for word in sentiment_files: \n",
    "        score = sent_analyze.polarity_scores(word)\n",
    "        score[\"sentiment_file\"] = word \n",
    "        results.append(score)\n",
    "    return(results)\n",
    "\n",
    "\n",
    "#Function 3: \n",
    "    #What the function does: to be creating a list of reviews, then joining the reviews together to a string and \n",
    "                             #getting a count for each word in the string\n",
    "    #Input: df and column \n",
    "    #Output: a dictionary with each word and the count of the word\n",
    "def creating_freq_list_from_df_to_dict(df, column):\n",
    "    reviews = df[column].tolist() \n",
    "    review_string = \" \".join(reviews)\n",
    "    review_string = review_string.split()\n",
    "    review_dict = Counter(review_string)\n",
    "    return review_dict\n",
    "\n",
    "#Function 3: \n",
    "    #What the function does: creates a word cloud that is in the shape of the mask passed in\n",
    "    #Input: the location where the mask image is saved, the frequency word dictionary, and the max # of words to include\n",
    "            #and the title of the plot \n",
    "def create_word_cloud_with_mask(path_of_mask_image, dictionary, \n",
    "                                max_num_words, title):\n",
    "        mask = np.array(Image.open(path_of_mask_image))\n",
    "        #creating the word cloud \n",
    "        word_cloud = WordCloud(background_color = \"white\", \n",
    "                               max_words = max_num_words, \n",
    "                              mask = mask, max_font_size = 125, \n",
    "                              random_state = 1006)\n",
    "        word_cloud.generate_from_frequencies(dictionary)\n",
    "        #creating the coloring for the word cloud \n",
    "        image_colors = ImageColorGenerator(mask)\n",
    "        plt.figure(figsize = [8,8])\n",
    "        plt.imshow(word_cloud.recolor(color_func = image_colors), \n",
    "                  interpolation = \"bilinear\")\n",
    "        plt.title(title)\n",
    "        sns.set_context(\"poster\")\n",
    "        plt.axis(\"off\")\n",
    "        return plt\n",
    "\n",
    "#Function 4: \n",
    "    #What the function does: creates a df with two columns: word and count of the top 12 words\n",
    "    #Input: the word frequency dictionary \n",
    "    #Output: a df with the top 12 words \n",
    "def word_freq_dict_to_df_top_words(dictionary, number_of_words_wanted): \n",
    "    df = pd.DataFrame.from_dict(dictionary,orient='index')\n",
    "    df.columns = [\"count\"]\n",
    "    df[\"word\"] = df.index\n",
    "    df.reset_index(drop = True, inplace = True)\n",
    "    df.sort_values(by=[\"count\"], ascending = False, inplace = True)\n",
    "    df = df[:number_of_words_wanted]\n",
    "    return(df)\n",
    "\n",
    "#Function 5: \n",
    "    #What the function does: creates a bar graph\n",
    "    #Input: the df and title of the graph \n",
    "    #Output: the bar graph\n",
    "def top_words_bar_plot(df, title): \n",
    "    with sns.plotting_context(\"talk\"):\n",
    "        graph = sns.barplot(y = \"count\", x = \"word\", data = df, \n",
    "                           palette = \"GnBu_d\")\n",
    "        plt.title(title)\n",
    "        plt.xlabel(\"Word\")\n",
    "        plt.ylabel(\"Count\")\n",
    "        plt.xticks(rotation = 90)\n",
    "        return plt\n",
    "\n",
    "#Function 6: \n",
    "    #What the function does: creates a df with two columns: word and count \n",
    "    #Input: the word frequency dictionary \n",
    "    #Output: a df\n",
    "def word_freq_dict_to_df_all_words(dictionary): \n",
    "    df = pd.DataFrame.from_dict(dictionary,orient='index')\n",
    "    df.columns = [\"count\"]\n",
    "    df[\"word\"] = df.index\n",
    "    df.reset_index(drop = True, inplace = True)\n",
    "    df.sort_values(by=[\"count\"], ascending = False, inplace = True)\n",
    "    return(df)\n",
    "    \n",
    "#Function 7: \n",
    "    #What the function does: Returns 2 statements: One with the total number of words and the other with the number \n",
    "                            #of unique words \n",
    "        #Input: the frequency count dictionary \n",
    "        #output: 2 statements \n",
    "def total_words_unique_words(dictionary): \n",
    "    eda_reviews_all_words = word_freq_dict_to_df_all_words(dictionary)\n",
    "    print(\"The total number of words is\", sum(eda_reviews_all_words[\"count\"]))\n",
    "    print(\"The total number of unique words is\", len(dictionary)) \n",
    "    \n",
    "#Function 8: \n",
    "    #What the function does: It duplicates the words in each review that are in all caps. \n",
    "    #Input: the review to be analyzed\n",
    "    #Output: a new review where the first words of the review are the duplicated words from all caps and \n",
    "            #then the original review follows\n",
    "def duplicate_all_cap_words(review): \n",
    "    capitalized_word = \"\"\n",
    "    for word in re.findall('([A-Z][A-Z]+\\w)', review):\n",
    "        if word in review: \n",
    "            capitalized_word = capitalized_word + \" \" + word\n",
    "    new_review = capitalized_word +\" \" + review\n",
    "    return new_review\n",
    "#Function 9: Weighted Title \n",
    "def duplicate_title(review): \n",
    "    new_review = review + \" \" + review +\" \" + review + \" \" + review\n",
    "    return(new_review)\n",
    "\n",
    "#Function 8: \n",
    "def get_count_of_all_cap_words(review): \n",
    "    count = 0 \n",
    "    for word in re.findall('([A-Z][A-Z]+\\w)', review):\n",
    "        if word in review: \n",
    "            count += 1\n",
    "    return count\n",
    "\n",
    "#Function 8: \n",
    "def get_count_of_all_words(review): \n",
    "    count = 0 \n",
    "    for word in re.findall('([A-z]+\\w)', review):\n",
    "        if word in review: \n",
    "            count += 1\n",
    "    return count\n",
    "\n",
    "#Function 9: \n",
    "    #What the function does: It removes all words that have less than 3 characters in it. \n",
    "    #Input: The string to have stopwords removed \n",
    "    #Ouptut: The string with the words with 2 or less characters removed \n",
    "def remove_words_less_than_3_characters(string):\n",
    "    new_string = \"\"\n",
    "    for word in re.findall('[A-z][A-z]+\\w', string): \n",
    "        new_string = new_string + \" \" + word\n",
    "    return new_string\n",
    "\n",
    "#Function 10: \n",
    "    #What the function does: Removes stopwords \n",
    "    #Input: a list of stopwords to be removed, the tokenized item that you want to remove stopwords in \n",
    "    #Output: the same item type back with the stopwords removed. \n",
    "def stop_word_removal(stopwords, item_that_you_want_to_remove_stopwords_in): \n",
    "    removed_stopwords = [] \n",
    "    for word in item_that_you_want_to_remove_stopwords_in: \n",
    "        if word in stopwords: \n",
    "            continue\n",
    "        if word not in stopwords: \n",
    "            removed_stopwords.append(word)\n",
    "    return(removed_stopwords)\n",
    "\n",
    "#Function11: \n",
    "    #What the function does: It takes the tokens from the df and joins it into a string, then replaces the \",\" with a space\n",
    "    #Input: the df and column to be changed \n",
    "    #Output: the data untokenized \n",
    "def getting_data_ready_for_freq(df, column): \n",
    "    df[column] = df[column].apply(\",\".join)\n",
    "    df[column] = df[column].str.replace(\",\", \" \")\n",
    "    return(df[column])\n",
    "\n",
    "#Function 12: \n",
    "    #What the function does: Takes the words in a column and uses the SentimentInstensityAnalyzer from nltk and \n",
    "                             #gets the sentiment score for every word in the column. If the word has a sentiment \n",
    "                             #score greater than or equal to .3 (max is 1) or less than or equal to -.3 (-1 is min)\n",
    "                             #the word is added to the keep_words list if not the word will be removed. \n",
    "def pos_neg_words(column):\n",
    "    sia = SIA()\n",
    "    keep_words = []\n",
    "\n",
    "    for word in column:\n",
    "        if (sia.polarity_scores(word)['compound']) >= 0.005:\n",
    "            keep_words.append(word)\n",
    "        elif (sia.polarity_scores(word)['compound']) <= -0.005:\n",
    "            keep_words.append(word)\n",
    "        elif word == \"not\": \n",
    "            keep_words.append(word)\n",
    "        else:\n",
    "            continue               \n",
    "    return keep_words\n",
    "\n",
    "#Function 13: \n",
    "    #What the function does: It uses the Porter stemmer to stem each word in the column \n",
    "    #Input: the item that you want to be stemmed \n",
    "    #Output: the same item type back with the words stemmed \n",
    "def stem_fun(item_that_you_want_to_be_stemmed):     \n",
    "    stemmer = PorterStemmer() \n",
    "    stemmed = [stemmer.stem(token) for token in item_that_you_want_to_be_stemmed]\n",
    "    return(stemmed)\n",
    "\n",
    "#Function 14: \n",
    "    #What the function does: It lemmatizes the data without using pos, meaning that it will not be as efficient\n",
    "    #Input: item to be lemmatized (the column)\n",
    "    #Output: the column lemmatized \n",
    "def lemma_func(item_to_lemmatize):\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    lemmatized_review = []\n",
    "    for token in item_to_lemmatize: \n",
    "        word = lemmatizer.lemmatize(token)\n",
    "        lemmatized_review.append(word)\n",
    "    return lemmatized_review\n",
    "\n",
    "#Function 15: \n",
    "    #What the function does: Creates bigrams from a tokenized column in a dataframe\n",
    "    #Input: the column that you want to create a ngram with \n",
    "    #Output: a list of ngrams\n",
    "def creating_ngrams(item_to_be_ngrammed, number_of_ngram):\n",
    "    # zip function helps generate ngrams\n",
    "    ngrams = zip(*[item_to_be_ngrammed[i:] for i in range(number_of_ngram)])\n",
    "    # Concatentate the tokens into ngrams and return\n",
    "    return [\"_\".join(ngram) for ngram in ngrams]\n",
    "\n",
    "#Function 16: \n",
    "    #What the function does: Create a bag of words from a column in a df... \n",
    "    #Input: df and column to be transformed \n",
    "    #Output: A list of dictionaries for each row in the df that contains the word as a key and the count as the value \n",
    "def bag_of_words(df, column_to_be_bagged):\n",
    "    bag_of_words = [] \n",
    "    from collections import Counter \n",
    "    for word in df[column_to_be_bagged]: \n",
    "        bag_of_words.append(Counter(word))\n",
    "    return bag_of_words\n",
    "\n",
    "#Function 17: \n",
    "    #What the function does: Takes the bag of words and makes it into a giant sparse matrix df, with 0s where nas are\n",
    "    #Input: bag of words \n",
    "    #Output: Giant df with the words as column names and counts as row entries \n",
    "def bow_to_df(bag_of_words): \n",
    "    df = pd.DataFrame.from_records(bag_of_words)\n",
    "    df = df.fillna(0).astype(int)\n",
    "    return(df)\n",
    "\n",
    "#Function 18: \n",
    "    #What the function does: It normalizing the df by getting the sum of each row and then dividing every entry by \n",
    "                             #the sum, resulting in the percentage make-up of each word\n",
    "    #Input: dataframe to be normalized \n",
    "    #Output: normalized dataframe \n",
    "def normalize_df(df):\n",
    "    names = df.columns \n",
    "    df[\"total\"] = df.sum(axis = 1)\n",
    "    for name in names: \n",
    "        df[name] = df[name]/df[\"total\"]\n",
    "    return(df)\n",
    "\n",
    "#Function 19: \n",
    "    #What the function does: Creates a confusion matrix graph \n",
    "    #Input: the confusion matrix, accuracy_label, and type of df \n",
    "    #Output: Confusion matrix graph\n",
    "def confusion_matrix_graph (cm, accuracy_label, type_of_df): \n",
    "    g = plt.figure(figsize=(8, 8))\n",
    "    g = sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', cbar = False);\n",
    "    g = plt.ylabel('Actual');\n",
    "    g = plt.xlabel('Predicted');\n",
    "    g = all_sample_title = type_of_df +' Accuracy Score: {0}'.format(round(accuracy_label, 4))\n",
    "    g = plt.title(all_sample_title, size = 12);\n",
    "    return(g)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First step is importing the documents\n",
    "#Getting a list of all the file names in my pos file \n",
    "positive = os.listdir(\"C:\\\\Users\\\\ho511\\\\Desktop\\\\IST_736\\\\homeworks\\\\week_3\\\\harry_potter_corpus\\\\pos\")\n",
    "positive_path = \"C:\\\\Users\\\\ho511\\\\Desktop\\\\IST_736\\\\homeworks\\\\week_3\\\\harry_potter_corpus\\\\pos\"\n",
    "\n",
    "#Getting a list of all the file names in my neg file \n",
    "negative = os.listdir(\"C:\\\\Users\\\\ho511\\\\Desktop\\\\IST_736\\\\homeworks\\\\week_3\\\\harry_potter_corpus\\\\neg\")\n",
    "negative_path = \"C:\\\\Users\\\\ho511\\\\Desktop\\\\IST_736\\\\homeworks\\\\week_3\\\\harry_potter_corpus\\\\neg\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### looking to see if I successfully got the file names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['10_hp1_0.txt', '10_hp1_1.txt', '10_hp1_10.txt', '10_hp1_11.txt', '10_hp1_12.txt', '10_hp1_13.txt', '10_hp1_14.txt', '10_hp1_15.txt', '10_hp1_16.txt', '10_hp1_18.txt']\n"
     ]
    }
   ],
   "source": [
    "print(positive[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### looking at the first 10 items in the negative list to ensure I successfully extracted the file names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1_hp1_1.txt', '1_hp1_10.txt', '1_hp1_11.txt', '1_hp1_12.txt', '1_hp1_13.txt', '1_hp1_14.txt', '1_hp1_15.txt', '1_hp1_16.txt', '1_hp1_17.txt', '1_hp1_18.txt']\n"
     ]
    }
   ],
   "source": [
    "print(negative[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using my reading_in_file function to read in all of the positive files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_reviews = reading_in_files(positive, positive_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confirming that the function worked by looking at the first review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"10** Harry Potter and the Sorcerer's Stone\\n== 10/10 - an unforgettable start to a fantastic film series and the career of the impeccable Emma Watson (and the other kids)\""
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_reviews[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The score is separated from the review by **, the title is separated from the review on ==. It is important to note, that if I split on the **, it is possible that the review will also be split. There is a high likelihood that the \"**\" characters are included in some of the review text as well. This will have to be addressed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_reviews = list_split(positive_reviews, \"**\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking to make sure that it worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['10',\n",
       " \" Harry Potter and the Sorcerer's Stone\\n== 10/10 - an unforgettable start to a fantastic film series and the career of the impeccable Emma Watson (and the other kids)\"]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_reviews[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changing the positive_review list to a data frame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df = pd.DataFrame(positive_reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at the first 5 rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>The Magic Comes To Life!\\n== Once upon a time...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>Pure Magic\\n== This movie is a delight for th...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n==...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n== 10/...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>Great Journey to the Magic World\\n== I watch ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>487</td>\n",
       "      <td>9</td>\n",
       "      <td>I'm Sorry, Hermoine and Ron Who?\\n== The one ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>488</td>\n",
       "      <td>9</td>\n",
       "      <td>A fantastic ending\\n== This movie was the per...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>489</td>\n",
       "      <td>9</td>\n",
       "      <td>LEGENDARY!\\n== Best cinematic universe out th...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>9</td>\n",
       "      <td>Good but ending changed for worse\\n== I was r...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>9</td>\n",
       "      <td>Great conclusion, of fantastic story\\n== i wa...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>492 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0                                                  1     2     3     4   \\\n",
       "0    10   The Magic Comes To Life!\\n== Once upon a time...  None  None  None   \n",
       "1    10   Pure Magic\\n== This movie is a delight for th...  None  None  None   \n",
       "2    10   Enchantment, Trapdoor to Imaginary World.\\n==...  None  None  None   \n",
       "3    10   Harry Potter and the Sorcerer's Stone\\n== 10/...  None  None  None   \n",
       "4    10   Great Journey to the Magic World\\n== I watch ...  None  None  None   \n",
       "..   ..                                                ...   ...   ...   ...   \n",
       "487   9   I'm Sorry, Hermoine and Ron Who?\\n== The one ...  None  None  None   \n",
       "488   9   A fantastic ending\\n== This movie was the per...  None  None  None   \n",
       "489   9   LEGENDARY!\\n== Best cinematic universe out th...  None  None  None   \n",
       "490   9   Good but ending changed for worse\\n== I was r...  None  None  None   \n",
       "491   9   Great conclusion, of fantastic story\\n== i wa...  None  None  None   \n",
       "\n",
       "       5     6     7     8     9   ...    18    19    20    21    22    23  \\\n",
       "0    None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "1    None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "2    None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "3    None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "4    None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "..    ...   ...   ...   ...   ...  ...   ...   ...   ...   ...   ...   ...   \n",
       "487  None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "488  None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "489  None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "490  None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "491  None  None  None  None  None  ...  None  None  None  None  None  None   \n",
       "\n",
       "       24    25    26    27  \n",
       "0    None  None  None  None  \n",
       "1    None  None  None  None  \n",
       "2    None  None  None  None  \n",
       "3    None  None  None  None  \n",
       "4    None  None  None  None  \n",
       "..    ...   ...   ...   ...  \n",
       "487  None  None  None  None  \n",
       "488  None  None  None  None  \n",
       "489  None  None  None  None  \n",
       "490  None  None  None  None  \n",
       "491  None  None  None  None  \n",
       "\n",
       "[492 rows x 28 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"review\"] = positive_df[positive_df.columns[1:]].apply(lambda row: \" \".join(row.dropna().astype(str)), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_title = positive_df[\"review\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" Harry Potter and the Sorcerer's Stone\\n== 10/10 - an unforgettable start to a fantastic film series and the career of the impeccable Emma Watson (and the other kids)\""
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_title[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_title = list_split(positive_title, \"==\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df = pd.DataFrame(positive_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>The Magic Comes To Life!\\n</td>\n",
       "      <td>Once upon a time (and not that long ago), in ...</td>\n",
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       "    <tr>\n",
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       "      <td>Pure Magic\\n</td>\n",
       "      <td>This movie is a delight for those of all ages...</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n</td>\n",
       "      <td>This is not a film, it is a trap door to a li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n</td>\n",
       "      <td>10/10 - an unforgettable start to a fantastic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n</td>\n",
       "      <td>I watch this movie again in 2019, because i t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              0  \\\n",
       "0                    The Magic Comes To Life!\\n   \n",
       "1                                  Pure Magic\\n   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n   \n",
       "3       Harry Potter and the Sorcerer's Stone\\n   \n",
       "4            Great Journey to the Magic World\\n   \n",
       "\n",
       "                                                   1  \n",
       "0   Once upon a time (and not that long ago), in ...  \n",
       "1   This movie is a delight for those of all ages...  \n",
       "2   This is not a film, it is a trap door to a li...  \n",
       "3   10/10 - an unforgettable start to a fantastic...  \n",
       "4   I watch this movie again in 2019, because i t...  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df.columns = [\"title\", \"review\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This shows that my worry about the reviews being split on ** was warranted. I need to joing the review text to one column.  Positive, is the the score was successfully extracted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"title\"] = positive_df[\"title\"].apply(lambda row: duplicate_title(row)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>review</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "      <td>This movie is a delight for those of all ages...</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>This is not a film, it is a trap door to a li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>10/10 - an unforgettable start to a fantastic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
       "      <td>I watch this movie again in 2019, because i t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>487</td>\n",
       "      <td>I'm Sorry, Hermoine and Ron Who?\\n  I'm Sorry...</td>\n",
       "      <td>The one that ends it all, after 8 films (7 bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>488</td>\n",
       "      <td>A fantastic ending\\n  A fantastic ending\\n  A...</td>\n",
       "      <td>This movie was the perfect ending to the best...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>489</td>\n",
       "      <td>LEGENDARY!\\n  LEGENDARY!\\n  LEGENDARY!\\n  LEG...</td>\n",
       "      <td>Best cinematic universe out there! Thank you ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>Good but ending changed for worse\\n  Good but...</td>\n",
       "      <td>I was really looking forward to this movie. T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>Great conclusion, of fantastic story\\n  Great...</td>\n",
       "      <td>i was looking forward to seeing the last part...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>492 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  \\\n",
       "0     The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1     Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2     Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3     Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4     Great Journey to the Magic World\\n  Great Jou...   \n",
       "..                                                 ...   \n",
       "487   I'm Sorry, Hermoine and Ron Who?\\n  I'm Sorry...   \n",
       "488   A fantastic ending\\n  A fantastic ending\\n  A...   \n",
       "489   LEGENDARY!\\n  LEGENDARY!\\n  LEGENDARY!\\n  LEG...   \n",
       "490   Good but ending changed for worse\\n  Good but...   \n",
       "491   Great conclusion, of fantastic story\\n  Great...   \n",
       "\n",
       "                                                review  \n",
       "0     Once upon a time (and not that long ago), in ...  \n",
       "1     This movie is a delight for those of all ages...  \n",
       "2     This is not a film, it is a trap door to a li...  \n",
       "3     10/10 - an unforgettable start to a fantastic...  \n",
       "4     I watch this movie again in 2019, because i t...  \n",
       "..                                                 ...  \n",
       "487   The one that ends it all, after 8 films (7 bo...  \n",
       "488   This movie was the perfect ending to the best...  \n",
       "489   Best cinematic universe out there! Thank you ...  \n",
       "490   I was really looking forward to this movie. T...  \n",
       "491   i was looking forward to seeing the last part...  \n",
       "\n",
       "[492 rows x 2 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"review\"] = positive_df[positive_df.columns[:]].apply(lambda row: \" \".join(row.dropna().astype(str)), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df.drop(\"title\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changing the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...\n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...\n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...\n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...\n",
       "4   Great Journey to the Magic World\\n  Great Jou..."
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Repeating the process for the negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_reviews = reading_in_files(negative, negative_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1',\n",
       " ' What a Disappointment\\n== This movie is a disgrace to the series. The consistency issues in regards to the book and the first two films are readily apparent. Even to fans who have not read the books, the change in the geography of Hogwarts and how the characters dress are easily recognized as a break in continuity.The screenplay is chunked and leaves many important pieces to the overall storyline unexplained. Needless to say, the scenes do not flow very well and it leaves the non-reader confused in many places.This movie will unfortunately stick out like a sore thumb if the following films stick to the wonderfully developed world of Harry Potter.']"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_reviews[40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_reviews = list_split(negative_reviews, \"**\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1',\n",
       " ' For Kids Only, Unfortunately\\n== ',\n",
       " '*SLIGHT SPOILERS',\n",
       " '* As an adult, it didn\\'t appeal to me in the least. The effects were poorly done, the giant plot leap in the middle (\"lots of strange things are happening-- hey, I know! it\\'s definitely because the creepy teacher guy wants the thing under the trap door under the three-headed dog\") threw me completely out of the story. It\\'s really a kids only movie, which disappointed me, because I\\'d been hearing such good things about it. And then at the end, Harry is sent back to his abusive adoptive parents. So much for changing one\\'s situation.And I know that they had to cut tremendous amounts of story in order to make it only 2 1/2 hours long (which was still way too long for this movie). After waiting in line outside the theater with tickets bought a week ahead of time, I must say I was truly disappointed. I\\'m even more saddened by the commercial success and the lunacy of die hard Harry Potter fans which will keep this sad and poorly-written movie in theaters much longer than it needs to be.Some books should remain books.']"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_reviews[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>For Kids Only, Unfortunately\\n==</td>\n",
       "      <td>*SLIGHT SPOILERS</td>\n",
       "      <td>* As an adult, it didn't appeal to me in the l...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>An effects-laden excuse of an adaptation\\n== ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Hollywood's greatest shame.\\n== At first I li...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Dismal, Contrived, Ripoff and just plain dumb...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Great storytelling, no story\\n==</td>\n",
       "      <td>SPOILERS</td>\n",
       "      <td>Before I briefly state my views, I must confes...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  0                                                  1                 2   \\\n",
       "0  1                  For Kids Only, Unfortunately\\n==   *SLIGHT SPOILERS   \n",
       "1  1   An effects-laden excuse of an adaptation\\n== ...              None   \n",
       "2  1   Hollywood's greatest shame.\\n== At first I li...              None   \n",
       "3  1   Dismal, Contrived, Ripoff and just plain dumb...              None   \n",
       "4  1                  Great storytelling, no story\\n==           SPOILERS   \n",
       "\n",
       "                                                  3     4     5     6     7   \\\n",
       "0  * As an adult, it didn't appeal to me in the l...  None  None  None  None   \n",
       "1                                               None  None  None  None  None   \n",
       "2                                               None  None  None  None  None   \n",
       "3                                               None  None  None  None  None   \n",
       "4  Before I briefly state my views, I must confes...              None  None   \n",
       "\n",
       "     8     9   ...    16    17    18    19    20    21    22    23    24    25  \n",
       "0  None  None  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "1  None  None  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "2  None  None  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "3  None  None  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "4  None  None  ...  None  None  None  None  None  None  None  None  None  None  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df = pd.DataFrame(negative_reviews)\n",
    "negative_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492, 26)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"review\"] = negative_df[negative_df.columns[1:]].apply(lambda row: \" \".join(row.dropna().astype(str)), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_title = negative_df[\"review\"].tolist()\n",
    "negative_title = list_split(negative_title, \"==\")\n",
    "negative_df = pd.DataFrame(negative_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>For Kids Only, Unfortunately\\n</td>\n",
       "      <td>*SLIGHT SPOILERS * As an adult, it didn't ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>An effects-laden excuse of an adaptation\\n</td>\n",
       "      <td>Many viewers of this film applaud its faithfu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Hollywood's greatest shame.\\n</td>\n",
       "      <td>At first I liked the Harry Potter series, it ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dismal, Contrived, Ripoff and just plain dumb!\\n</td>\n",
       "      <td>terribly contrived and juvenile.  From the ou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great storytelling, no story\\n</td>\n",
       "      <td>SPOILERS Before I briefly state my views, I ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0  \\\n",
       "0                     For Kids Only, Unfortunately\\n   \n",
       "1         An effects-laden excuse of an adaptation\\n   \n",
       "2                      Hollywood's greatest shame.\\n   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb!\\n   \n",
       "4                     Great storytelling, no story\\n   \n",
       "\n",
       "                                                   1  \n",
       "0    *SLIGHT SPOILERS * As an adult, it didn't ap...  \n",
       "1   Many viewers of this film applaud its faithfu...  \n",
       "2   At first I liked the Harry Potter series, it ...  \n",
       "3   terribly contrived and juvenile.  From the ou...  \n",
       "4    SPOILERS Before I briefly state my views, I ...  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df.columns = [\"title\", \"review\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"title\"] = negative_df[\"title\"].apply(lambda row: duplicate_title(row)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"review\"] = negative_df[negative_df.columns[:]].apply(lambda row: \" \".join(row.dropna().astype(str)), axis = 1)\n",
    "negative_df.drop(\"title\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>review</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>For Kids Only, Unfortunately\\n  For Kids Only...</td>\n",
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       "    <tr>\n",
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       "      <td>An effects-laden excuse of an adaptation\\n  A...</td>\n",
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       "      <td>Hollywood's greatest shame.\\n  Hollywood's gr...</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dismal, Contrived, Ripoff and just plain dumb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great storytelling, no story\\n  Great storyte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>487</td>\n",
       "      <td>Deathly Disappointing\\n  Deathly Disappointin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>488</td>\n",
       "      <td>Nothing but disappointing.\\n  Nothing but dis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>489</td>\n",
       "      <td>A middling series ends on a low-point.\\n  A m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>OK but not I'm not Wowed!\\n  OK but not I'm n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>Its OK, not great.\\n  Its OK, not great.\\n  I...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>492 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                review\n",
       "0     For Kids Only, Unfortunately\\n  For Kids Only...\n",
       "1     An effects-laden excuse of an adaptation\\n  A...\n",
       "2     Hollywood's greatest shame.\\n  Hollywood's gr...\n",
       "3     Dismal, Contrived, Ripoff and just plain dumb...\n",
       "4     Great storytelling, no story\\n  Great storyte...\n",
       "..                                                 ...\n",
       "487   Deathly Disappointing\\n  Deathly Disappointin...\n",
       "488   Nothing but disappointing.\\n  Nothing but dis...\n",
       "489   A middling series ends on a low-point.\\n  A m...\n",
       "490   OK but not I'm not Wowed!\\n  OK but not I'm n...\n",
       "491   Its OK, not great.\\n  Its OK, not great.\\n  I...\n",
       "\n",
       "[492 rows x 1 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA for positive reviews and negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492, 1)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492, 1)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *There is an equal number of positive and negative reviews in the data frames. Each data frame has 2 columns.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using my creating_freq_list_from_df_to_dict function "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As part of the cleansing of the reviews, I am going to turn everything into lowercase, however, I do not want to lose the emphasis of words written in ALL CAPS. Therefore, I created a function, that will duplicate every word in the review that is in all caps. I think that it is fair, to count words in all caps as double."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"new_review\"] = positive_df.apply(lambda row: duplicate_all_cap_words(row[\"review\"]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>IAN  Enchantment, Trapdoor to Imaginary World...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Har...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>The beginning of a magical journey\\n  The beg...</td>\n",
       "      <td>The beginning of a magical journey\\n  The be...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Very Book Accurate and Enjoyable!!\\n  Very Bo...</td>\n",
       "      <td>VERY LOVES  Very Book Accurate and Enjoyable!...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Great\\n  Great\\n  Great\\n  Great\\n  It brough...</td>\n",
       "      <td>Great\\n  Great\\n  Great\\n  Great\\n  It broug...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>One of my favorites\\n  One of my favorites\\n ...</td>\n",
       "      <td>JKR  One of my favorites\\n  One of my favorit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Absolutely amazing\\n  Absolutely amazing\\n  A...</td>\n",
       "      <td>Absolutely amazing\\n  Absolutely amazing\\n  ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "5   The beginning of a magical journey\\n  The beg...   \n",
       "6   Very Book Accurate and Enjoyable!!\\n  Very Bo...   \n",
       "7   Great\\n  Great\\n  Great\\n  Great\\n  It brough...   \n",
       "8   One of my favorites\\n  One of my favorites\\n ...   \n",
       "9   Absolutely amazing\\n  Absolutely amazing\\n  A...   \n",
       "\n",
       "                                          new_review  \n",
       "0    The Magic Comes To Life!\\n  The Magic Comes ...  \n",
       "1    Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pu...  \n",
       "2   IAN  Enchantment, Trapdoor to Imaginary World...  \n",
       "3    Harry Potter and the Sorcerer's Stone\\n  Har...  \n",
       "4    Great Journey to the Magic World\\n  Great Jo...  \n",
       "5    The beginning of a magical journey\\n  The be...  \n",
       "6   VERY LOVES  Very Book Accurate and Enjoyable!...  \n",
       "7    Great\\n  Great\\n  Great\\n  Great\\n  It broug...  \n",
       "8   JKR  One of my favorites\\n  One of my favorit...  \n",
       "9    Absolutely amazing\\n  Absolutely amazing\\n  ...  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"cap_count\"] = positive_df.apply(lambda row: get_count_of_all_cap_words(row[\"review\"]), axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>IAN  Enchantment, Trapdoor to Imaginary World...</td>\n",
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       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
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       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>The beginning of a magical journey\\n  The beg...</td>\n",
       "      <td>The beginning of a magical journey\\n  The be...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Very Book Accurate and Enjoyable!!\\n  Very Bo...</td>\n",
       "      <td>VERY LOVES  Very Book Accurate and Enjoyable!...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Great\\n  Great\\n  Great\\n  Great\\n  It brough...</td>\n",
       "      <td>Great\\n  Great\\n  Great\\n  Great\\n  It broug...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>One of my favorites\\n  One of my favorites\\n ...</td>\n",
       "      <td>JKR  One of my favorites\\n  One of my favorit...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Absolutely amazing\\n  Absolutely amazing\\n  A...</td>\n",
       "      <td>Absolutely amazing\\n  Absolutely amazing\\n  ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "5   The beginning of a magical journey\\n  The beg...   \n",
       "6   Very Book Accurate and Enjoyable!!\\n  Very Bo...   \n",
       "7   Great\\n  Great\\n  Great\\n  Great\\n  It brough...   \n",
       "8   One of my favorites\\n  One of my favorites\\n ...   \n",
       "9   Absolutely amazing\\n  Absolutely amazing\\n  A...   \n",
       "\n",
       "                                          new_review  cap_count  \n",
       "0    The Magic Comes To Life!\\n  The Magic Comes ...          0  \n",
       "1    Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pu...          0  \n",
       "2   IAN  Enchantment, Trapdoor to Imaginary World...          1  \n",
       "3    Harry Potter and the Sorcerer's Stone\\n  Har...          0  \n",
       "4    Great Journey to the Magic World\\n  Great Jo...          0  \n",
       "5    The beginning of a magical journey\\n  The be...          0  \n",
       "6   VERY LOVES  Very Book Accurate and Enjoyable!...          2  \n",
       "7    Great\\n  Great\\n  Great\\n  Great\\n  It broug...          0  \n",
       "8   JKR  One of my favorites\\n  One of my favorit...          1  \n",
       "9    Absolutely amazing\\n  Absolutely amazing\\n  ...          0  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"all_words_count\"] = positive_df.apply(lambda row: get_count_of_all_words(row[\"review\"]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
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       "      <td>461</td>\n",
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       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Har...</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
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       "      <td>0</td>\n",
       "      <td>94</td>\n",
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       "      <td>5</td>\n",
       "      <td>The beginning of a magical journey\\n  The beg...</td>\n",
       "      <td>The beginning of a magical journey\\n  The be...</td>\n",
       "      <td>0</td>\n",
       "      <td>597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Very Book Accurate and Enjoyable!!\\n  Very Bo...</td>\n",
       "      <td>VERY LOVES  Very Book Accurate and Enjoyable!...</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Great\\n  Great\\n  Great\\n  Great\\n  It brough...</td>\n",
       "      <td>Great\\n  Great\\n  Great\\n  Great\\n  It broug...</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>One of my favorites\\n  One of my favorites\\n ...</td>\n",
       "      <td>JKR  One of my favorites\\n  One of my favorit...</td>\n",
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       "      <td>79</td>\n",
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       "      <td>9</td>\n",
       "      <td>Absolutely amazing\\n  Absolutely amazing\\n  A...</td>\n",
       "      <td>Absolutely amazing\\n  Absolutely amazing\\n  ...</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
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      "text/plain": [
       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "5   The beginning of a magical journey\\n  The beg...   \n",
       "6   Very Book Accurate and Enjoyable!!\\n  Very Bo...   \n",
       "7   Great\\n  Great\\n  Great\\n  Great\\n  It brough...   \n",
       "8   One of my favorites\\n  One of my favorites\\n ...   \n",
       "9   Absolutely amazing\\n  Absolutely amazing\\n  A...   \n",
       "\n",
       "                                          new_review  cap_count  \\\n",
       "0    The Magic Comes To Life!\\n  The Magic Comes ...          0   \n",
       "1    Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pu...          0   \n",
       "2   IAN  Enchantment, Trapdoor to Imaginary World...          1   \n",
       "3    Harry Potter and the Sorcerer's Stone\\n  Har...          0   \n",
       "4    Great Journey to the Magic World\\n  Great Jo...          0   \n",
       "5    The beginning of a magical journey\\n  The be...          0   \n",
       "6   VERY LOVES  Very Book Accurate and Enjoyable!...          2   \n",
       "7    Great\\n  Great\\n  Great\\n  Great\\n  It broug...          0   \n",
       "8   JKR  One of my favorites\\n  One of my favorit...          1   \n",
       "9    Absolutely amazing\\n  Absolutely amazing\\n  ...          0   \n",
       "\n",
       "   all_words_count  \n",
       "0              969  \n",
       "1              104  \n",
       "2              461  \n",
       "3               43  \n",
       "4               94  \n",
       "5              597  \n",
       "6              112  \n",
       "7               17  \n",
       "8               79  \n",
       "9               19  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"percent_cap\"] = positive_df[\"cap_count\"]/positive_df[\"all_words_count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>cap_count</th>\n",
       "      <th>all_words_count</th>\n",
       "      <th>percent_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes ...</td>\n",
       "      <td>0</td>\n",
       "      <td>969</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pu...</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>IAN  Enchantment, Trapdoor to Imaginary World...</td>\n",
       "      <td>1</td>\n",
       "      <td>461</td>\n",
       "      <td>0.002169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Har...</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jo...</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>The beginning of a magical journey\\n  The beg...</td>\n",
       "      <td>The beginning of a magical journey\\n  The be...</td>\n",
       "      <td>0</td>\n",
       "      <td>597</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Very Book Accurate and Enjoyable!!\\n  Very Bo...</td>\n",
       "      <td>VERY LOVES  Very Book Accurate and Enjoyable!...</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>0.017857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Great\\n  Great\\n  Great\\n  Great\\n  It brough...</td>\n",
       "      <td>Great\\n  Great\\n  Great\\n  Great\\n  It broug...</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>One of my favorites\\n  One of my favorites\\n ...</td>\n",
       "      <td>JKR  One of my favorites\\n  One of my favorit...</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Absolutely amazing\\n  Absolutely amazing\\n  A...</td>\n",
       "      <td>Absolutely amazing\\n  Absolutely amazing\\n  ...</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "5   The beginning of a magical journey\\n  The beg...   \n",
       "6   Very Book Accurate and Enjoyable!!\\n  Very Bo...   \n",
       "7   Great\\n  Great\\n  Great\\n  Great\\n  It brough...   \n",
       "8   One of my favorites\\n  One of my favorites\\n ...   \n",
       "9   Absolutely amazing\\n  Absolutely amazing\\n  A...   \n",
       "\n",
       "                                          new_review  cap_count  \\\n",
       "0    The Magic Comes To Life!\\n  The Magic Comes ...          0   \n",
       "1    Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pu...          0   \n",
       "2   IAN  Enchantment, Trapdoor to Imaginary World...          1   \n",
       "3    Harry Potter and the Sorcerer's Stone\\n  Har...          0   \n",
       "4    Great Journey to the Magic World\\n  Great Jo...          0   \n",
       "5    The beginning of a magical journey\\n  The be...          0   \n",
       "6   VERY LOVES  Very Book Accurate and Enjoyable!...          2   \n",
       "7    Great\\n  Great\\n  Great\\n  Great\\n  It broug...          0   \n",
       "8   JKR  One of my favorites\\n  One of my favorit...          1   \n",
       "9    Absolutely amazing\\n  Absolutely amazing\\n  ...          0   \n",
       "\n",
       "   all_words_count  percent_cap  \n",
       "0              969     0.000000  \n",
       "1              104     0.000000  \n",
       "2              461     0.002169  \n",
       "3               43     0.000000  \n",
       "4               94     0.000000  \n",
       "5              597     0.000000  \n",
       "6              112     0.017857  \n",
       "7               17     0.000000  \n",
       "8               79     0.012658  \n",
       "9               19     0.000000  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df.drop([\"cap_count\", \"all_words_count\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "\n",
       "                                          new_review  percent_cap  \n",
       "0    The Magic Comes To Life!\\n  The Magic Comes ...     0.000000  \n",
       "1    Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pu...     0.000000  \n",
       "2   IAN  Enchantment, Trapdoor to Imaginary World...     0.002169  \n",
       "3    Harry Potter and the Sorcerer's Stone\\n  Har...     0.000000  \n",
       "4    Great Journey to the Magic World\\n  Great Jo...     0.000000  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>review</th>\n",
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       "    <tr>\n",
       "      <td>482</td>\n",
       "      <td>Review - Harry Potter and the Deathly Hallows...</td>\n",
       "      <td>Review - Harry Potter and the Deathly Hallow...</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>483</td>\n",
       "      <td>It's Official... Epic Movie, Epic Saga and Ep...</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>The end is good..\\n  The end is good..\\n  The...</td>\n",
       "      <td>The end is good..\\n  The end is good..\\n  Th...</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>486</td>\n",
       "      <td>They finally got it right!\\n  They finally go...</td>\n",
       "      <td>THIS HP7 THE  They finally got it right!\\n  T...</td>\n",
       "      <td>0.007092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>487</td>\n",
       "      <td>I'm Sorry, Hermoine and Ron Who?\\n  I'm Sorry...</td>\n",
       "      <td>I'm Sorry, Hermoine and Ron Who?\\n  I'm Sorr...</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>488</td>\n",
       "      <td>A fantastic ending\\n  A fantastic ending\\n  A...</td>\n",
       "      <td>A fantastic ending\\n  A fantastic ending\\n  ...</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>489</td>\n",
       "      <td>LEGENDARY!\\n  LEGENDARY!\\n  LEGENDARY!\\n  LEG...</td>\n",
       "      <td>LEGENDARY LEGENDARY LEGENDARY LEGENDARY  LEGE...</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>Good but ending changed for worse\\n  Good but...</td>\n",
       "      <td>Good but ending changed for worse\\n  Good bu...</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>Great conclusion, of fantastic story\\n  Great...</td>\n",
       "      <td>AWESOME  Great conclusion, of fantastic story...</td>\n",
       "      <td>0.005618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                review  \\\n",
       "482   Review - Harry Potter and the Deathly Hallows...   \n",
       "483   It's Official... Epic Movie, Epic Saga and Ep...   \n",
       "484   sadly disappointing\\n  sadly disappointing\\n ...   \n",
       "485   The end is good..\\n  The end is good..\\n  The...   \n",
       "486   They finally got it right!\\n  They finally go...   \n",
       "487   I'm Sorry, Hermoine and Ron Who?\\n  I'm Sorry...   \n",
       "488   A fantastic ending\\n  A fantastic ending\\n  A...   \n",
       "489   LEGENDARY!\\n  LEGENDARY!\\n  LEGENDARY!\\n  LEG...   \n",
       "490   Good but ending changed for worse\\n  Good but...   \n",
       "491   Great conclusion, of fantastic story\\n  Great...   \n",
       "\n",
       "                                            new_review  percent_cap  \n",
       "482    Review - Harry Potter and the Deathly Hallow...     0.000000  \n",
       "483    It's Official... Epic Movie, Epic Saga and E...     0.000000  \n",
       "484    sadly disappointing\\n  sadly disappointing\\n...     0.000000  \n",
       "485    The end is good..\\n  The end is good..\\n  Th...     0.000000  \n",
       "486   THIS HP7 THE  They finally got it right!\\n  T...     0.007092  \n",
       "487    I'm Sorry, Hermoine and Ron Who?\\n  I'm Sorr...     0.000000  \n",
       "488    A fantastic ending\\n  A fantastic ending\\n  ...     0.000000  \n",
       "489   LEGENDARY LEGENDARY LEGENDARY LEGENDARY  LEGE...     0.333333  \n",
       "490    Good but ending changed for worse\\n  Good bu...     0.000000  \n",
       "491   AWESOME  Great conclusion, of fantastic story...     0.005618  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### repeating the process for the negative reviews df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"new_review\"] = negative_df.apply(lambda row: duplicate_all_cap_words(row[\"review\"]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"cap_count\"] = negative_df.apply(lambda row: get_count_of_all_cap_words(row[\"review\"]), axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"all_words_count\"] = negative_df.apply(lambda row: get_count_of_all_words(row[\"review\"]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>For Kids Only, Unfortunately\\n  For Kids Only...</td>\n",
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       "      <td>197</td>\n",
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       "      <td>189</td>\n",
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       "      <td>4</td>\n",
       "      <td>Great storytelling, no story\\n  Great storyte...</td>\n",
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       "      <td>5</td>\n",
       "      <td>It's bland like drinking warm water\\n  It's b...</td>\n",
       "      <td>It's bland like drinking warm water\\n  It's ...</td>\n",
       "      <td>0</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Harry Potter and the Chamber of Commerce\\n  H...</td>\n",
       "      <td>Harry Potter and the Chamber of Commerce\\n  ...</td>\n",
       "      <td>0</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>its not worth the piece of paper the ticket w...</td>\n",
       "      <td>its not worth the piece of paper the ticket ...</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>My Revenge for the Non Fans!!\\n  My Revenge f...</td>\n",
       "      <td>AOL  My Revenge for the Non Fans!!\\n  My Reve...</td>\n",
       "      <td>1</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Terrible Terrible film\\n  Terrible Terrible f...</td>\n",
       "      <td>Terrible Terrible film\\n  Terrible Terrible ...</td>\n",
       "      <td>0</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb...   \n",
       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "5   It's bland like drinking warm water\\n  It's b...   \n",
       "6   Harry Potter and the Chamber of Commerce\\n  H...   \n",
       "7   its not worth the piece of paper the ticket w...   \n",
       "8   My Revenge for the Non Fans!!\\n  My Revenge f...   \n",
       "9   Terrible Terrible film\\n  Terrible Terrible f...   \n",
       "\n",
       "                                          new_review  cap_count  \\\n",
       "0   SLIGHT SPOILERS  For Kids Only, Unfortunately...          2   \n",
       "1    An effects-laden excuse of an adaptation\\n  ...          0   \n",
       "2   WOW HAD RINGWRAITHS ENTZ SHELOB YEAH  Hollywo...          6   \n",
       "3   JRR  Dismal, Contrived, Ripoff and just plain...          1   \n",
       "4   SPOILERS LOT  Great storytelling, no story\\n ...          2   \n",
       "5    It's bland like drinking warm water\\n  It's ...          0   \n",
       "6    Harry Potter and the Chamber of Commerce\\n  ...          0   \n",
       "7    its not worth the piece of paper the ticket ...          0   \n",
       "8   AOL  My Revenge for the Non Fans!!\\n  My Reve...          1   \n",
       "9    Terrible Terrible film\\n  Terrible Terrible ...          0   \n",
       "\n",
       "   all_words_count  \n",
       "0              197  \n",
       "1              191  \n",
       "2              598  \n",
       "3              189  \n",
       "4              612  \n",
       "5              384  \n",
       "6              326  \n",
       "7              159  \n",
       "8              367  \n",
       "9              330  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"percent_cap\"] = negative_df[\"cap_count\"]/negative_df[\"all_words_count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
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       "      <td>367</td>\n",
       "      <td>0.002725</td>\n",
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       "      <td>9</td>\n",
       "      <td>Terrible Terrible film\\n  Terrible Terrible f...</td>\n",
       "      <td>Terrible Terrible film\\n  Terrible Terrible ...</td>\n",
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       "      <td>330</td>\n",
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       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb...   \n",
       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "5   It's bland like drinking warm water\\n  It's b...   \n",
       "6   Harry Potter and the Chamber of Commerce\\n  H...   \n",
       "7   its not worth the piece of paper the ticket w...   \n",
       "8   My Revenge for the Non Fans!!\\n  My Revenge f...   \n",
       "9   Terrible Terrible film\\n  Terrible Terrible f...   \n",
       "\n",
       "                                          new_review  cap_count  \\\n",
       "0   SLIGHT SPOILERS  For Kids Only, Unfortunately...          2   \n",
       "1    An effects-laden excuse of an adaptation\\n  ...          0   \n",
       "2   WOW HAD RINGWRAITHS ENTZ SHELOB YEAH  Hollywo...          6   \n",
       "3   JRR  Dismal, Contrived, Ripoff and just plain...          1   \n",
       "4   SPOILERS LOT  Great storytelling, no story\\n ...          2   \n",
       "5    It's bland like drinking warm water\\n  It's ...          0   \n",
       "6    Harry Potter and the Chamber of Commerce\\n  ...          0   \n",
       "7    its not worth the piece of paper the ticket ...          0   \n",
       "8   AOL  My Revenge for the Non Fans!!\\n  My Reve...          1   \n",
       "9    Terrible Terrible film\\n  Terrible Terrible ...          0   \n",
       "\n",
       "   all_words_count  percent_cap  \n",
       "0              197     0.010152  \n",
       "1              191     0.000000  \n",
       "2              598     0.010033  \n",
       "3              189     0.005291  \n",
       "4              612     0.003268  \n",
       "5              384     0.000000  \n",
       "6              326     0.000000  \n",
       "7              159     0.000000  \n",
       "8              367     0.002725  \n",
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     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "negative_df.head(10)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df.drop([\"cap_count\", \"all_words_count\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
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       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb...   \n",
       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "5   It's bland like drinking warm water\\n  It's b...   \n",
       "6   Harry Potter and the Chamber of Commerce\\n  H...   \n",
       "7   its not worth the piece of paper the ticket w...   \n",
       "8   My Revenge for the Non Fans!!\\n  My Revenge f...   \n",
       "9   Terrible Terrible film\\n  Terrible Terrible f...   \n",
       "\n",
       "                                          new_review  percent_cap  \n",
       "0   SLIGHT SPOILERS  For Kids Only, Unfortunately...     0.010152  \n",
       "1    An effects-laden excuse of an adaptation\\n  ...     0.000000  \n",
       "2   WOW HAD RINGWRAITHS ENTZ SHELOB YEAH  Hollywo...     0.010033  \n",
       "3   JRR  Dismal, Contrived, Ripoff and just plain...     0.005291  \n",
       "4   SPOILERS LOT  Great storytelling, no story\\n ...     0.003268  \n",
       "5    It's bland like drinking warm water\\n  It's ...     0.000000  \n",
       "6    Harry Potter and the Chamber of Commerce\\n  ...     0.000000  \n",
       "7    its not worth the piece of paper the ticket ...     0.000000  \n",
       "8   AOL  My Revenge for the Non Fans!!\\n  My Reve...     0.002725  \n",
       "9    Terrible Terrible film\\n  Terrible Terrible ...     0.000000  "
      ]
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     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You can see that some of the reviews contain words in all caps. Unfortunately not all of the words give insight to the sentiment of the review. For example, in review 508 the reviewer capitalized the works THIS HP7 and THE, none of which give added insight into the words. It might be interesting to get a count of the number of words that were in all caps for each review. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making everything lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"new_review\"] = positive_df[\"new_review\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "      <td>the magic comes to life!\\n  the magic comes ...</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>5</td>\n",
       "      <td>The beginning of a magical journey\\n  The beg...</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>Very Book Accurate and Enjoyable!!\\n  Very Bo...</td>\n",
       "      <td>very loves  very book accurate and enjoyable!...</td>\n",
       "      <td>0.017857</td>\n",
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       "      <td>7</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>One of my favorites\\n  One of my favorites\\n ...</td>\n",
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       "      <td>0.012658</td>\n",
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       "      <td>Absolutely amazing\\n  Absolutely amazing\\n  A...</td>\n",
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      "text/plain": [
       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "5   The beginning of a magical journey\\n  The beg...   \n",
       "6   Very Book Accurate and Enjoyable!!\\n  Very Bo...   \n",
       "7   Great\\n  Great\\n  Great\\n  Great\\n  It brough...   \n",
       "8   One of my favorites\\n  One of my favorites\\n ...   \n",
       "9   Absolutely amazing\\n  Absolutely amazing\\n  A...   \n",
       "\n",
       "                                          new_review  percent_cap  \n",
       "0    the magic comes to life!\\n  the magic comes ...     0.000000  \n",
       "1    pure magic\\n  pure magic\\n  pure magic\\n  pu...     0.000000  \n",
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     "metadata": {},
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   "source": [
    "positive_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"new_review\"] = negative_df[\"new_review\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Hollywood's greatest shame.\\n  Hollywood's gr...</td>\n",
       "      <td>wow had ringwraiths entz shelob yeah  hollywo...</td>\n",
       "      <td>0.010033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dismal, Contrived, Ripoff and just plain dumb...</td>\n",
       "      <td>jrr  dismal, contrived, ripoff and just plain...</td>\n",
       "      <td>0.005291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great storytelling, no story\\n  Great storyte...</td>\n",
       "      <td>spoilers lot  great storytelling, no story\\n ...</td>\n",
       "      <td>0.003268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>It's bland like drinking warm water\\n  It's b...</td>\n",
       "      <td>it's bland like drinking warm water\\n  it's ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Harry Potter and the Chamber of Commerce\\n  H...</td>\n",
       "      <td>harry potter and the chamber of commerce\\n  ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>its not worth the piece of paper the ticket w...</td>\n",
       "      <td>its not worth the piece of paper the ticket ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>My Revenge for the Non Fans!!\\n  My Revenge f...</td>\n",
       "      <td>aol  my revenge for the non fans!!\\n  my reve...</td>\n",
       "      <td>0.002725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Terrible Terrible film\\n  Terrible Terrible f...</td>\n",
       "      <td>terrible terrible film\\n  terrible terrible ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb...   \n",
       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "5   It's bland like drinking warm water\\n  It's b...   \n",
       "6   Harry Potter and the Chamber of Commerce\\n  H...   \n",
       "7   its not worth the piece of paper the ticket w...   \n",
       "8   My Revenge for the Non Fans!!\\n  My Revenge f...   \n",
       "9   Terrible Terrible film\\n  Terrible Terrible f...   \n",
       "\n",
       "                                          new_review  percent_cap  \n",
       "0   slight spoilers  for kids only, unfortunately...     0.010152  \n",
       "1    an effects-laden excuse of an adaptation\\n  ...     0.000000  \n",
       "2   wow had ringwraiths entz shelob yeah  hollywo...     0.010033  \n",
       "3   jrr  dismal, contrived, ripoff and just plain...     0.005291  \n",
       "4   spoilers lot  great storytelling, no story\\n ...     0.003268  \n",
       "5    it's bland like drinking warm water\\n  it's ...     0.000000  \n",
       "6    harry potter and the chamber of commerce\\n  ...     0.000000  \n",
       "7    its not worth the piece of paper the ticket ...     0.000000  \n",
       "8   aol  my revenge for the non fans!!\\n  my reve...     0.002725  \n",
       "9    terrible terrible film\\n  terrible terrible ...     0.000000  "
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now, I need to clean the reviews and remove any non-alpha characters, and the new line character, and space character. First, I want to change anything that ends in n't to the word and then not "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"new_review\"] = positive_df[\"new_review\"].str.replace(r\"(n't)\", \"not\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"new_review\"] = positive_df[\"new_review\"].str.replace(r\"('m)\", \" am\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'd can mean had or would... I am going to change it to would beause I feel that is most likely the the correct use for reviews \n",
    "positive_df[\"new_review\"] = positive_df[\"new_review\"].str.replace(r\"('d)\", \" would\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"new_review\"] = positive_df[\"new_review\"].str.replace(r\"('ll)\", \" will\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I am removing 's, I do not feel like it will be useful for the review as it shows plural or possessive \n",
    "positive_df[\"new_review\"] = positive_df[\"new_review\"].str.replace(r\"('s)\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I want to keep the word not "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>percent_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "      <td>the magic comes to life!\\n  the magic comes ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "      <td>pure magic\\n  pure magic\\n  pure magic\\n  pu...</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>ian  enchantment, trapdoor to imaginary world...</td>\n",
       "      <td>0.002169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>harry potter and the sorcerer stone\\n  harry...</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
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       "      <td>0.000000</td>\n",
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      "text/plain": [
       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "\n",
       "                                          new_review  percent_cap  \n",
       "0    the magic comes to life!\\n  the magic comes ...     0.000000  \n",
       "1    pure magic\\n  pure magic\\n  pure magic\\n  pu...     0.000000  \n",
       "2   ian  enchantment, trapdoor to imaginary world...     0.002169  \n",
       "3    harry potter and the sorcerer stone\\n  harry...     0.000000  \n",
       "4    great journey to the magic world\\n  great jo...     0.000000  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"new_review\"] = negative_df[\"new_review\"].str.replace(r\"(n't)\", \"not\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"new_review\"] = negative_df[\"new_review\"].str.replace(r\"('m)\", \" am\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"new_review\"] = negative_df[\"new_review\"].str.replace(r\"('d)\", \" would\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"new_review\"] = negative_df[\"new_review\"].str.replace(r\"('ll)\", \" will\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"new_review\"] = negative_df[\"new_review\"].str.replace(r\"('s)\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>For Kids Only, Unfortunately\\n  For Kids Only...</td>\n",
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       "    <tr>\n",
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       "      <td>An effects-laden excuse of an adaptation\\n  A...</td>\n",
       "      <td>an effects-laden excuse of an adaptation\\n  ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
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       "      <td>2</td>\n",
       "      <td>Hollywood's greatest shame.\\n  Hollywood's gr...</td>\n",
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       "      <td>0.010033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dismal, Contrived, Ripoff and just plain dumb...</td>\n",
       "      <td>jrr  dismal, contrived, ripoff and just plain...</td>\n",
       "      <td>0.005291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great storytelling, no story\\n  Great storyte...</td>\n",
       "      <td>spoilers lot  great storytelling, no story\\n ...</td>\n",
       "      <td>0.003268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>It's bland like drinking warm water\\n  It's b...</td>\n",
       "      <td>it bland like drinking warm water\\n  it blan...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Harry Potter and the Chamber of Commerce\\n  H...</td>\n",
       "      <td>harry potter and the chamber of commerce\\n  ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>its not worth the piece of paper the ticket w...</td>\n",
       "      <td>its not worth the piece of paper the ticket ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>My Revenge for the Non Fans!!\\n  My Revenge f...</td>\n",
       "      <td>aol  my revenge for the non fans!!\\n  my reve...</td>\n",
       "      <td>0.002725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Terrible Terrible film\\n  Terrible Terrible f...</td>\n",
       "      <td>terrible terrible film\\n  terrible terrible ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb...   \n",
       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "5   It's bland like drinking warm water\\n  It's b...   \n",
       "6   Harry Potter and the Chamber of Commerce\\n  H...   \n",
       "7   its not worth the piece of paper the ticket w...   \n",
       "8   My Revenge for the Non Fans!!\\n  My Revenge f...   \n",
       "9   Terrible Terrible film\\n  Terrible Terrible f...   \n",
       "\n",
       "                                          new_review  percent_cap  \n",
       "0   slight spoilers  for kids only, unfortunately...     0.010152  \n",
       "1    an effects-laden excuse of an adaptation\\n  ...     0.000000  \n",
       "2   wow had ringwraiths entz shelob yeah  hollywo...     0.010033  \n",
       "3   jrr  dismal, contrived, ripoff and just plain...     0.005291  \n",
       "4   spoilers lot  great storytelling, no story\\n ...     0.003268  \n",
       "5    it bland like drinking warm water\\n  it blan...     0.000000  \n",
       "6    harry potter and the chamber of commerce\\n  ...     0.000000  \n",
       "7    its not worth the piece of paper the ticket ...     0.000000  \n",
       "8   aol  my revenge for the non fans!!\\n  my reve...     0.002725  \n",
       "9    terrible terrible film\\n  terrible terrible ...     0.000000  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time to remove any non-alpha characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"new_review\"] = positive_df[\"new_review\"].str.replace(r\"[^\\w^\\s]\", \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"new_review\"] = positive_df[\"new_review\"].str.replace('\\n',\" \") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>percent_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "      <td>the magic comes to life    the magic comes t...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "      <td>pure magic   pure magic   pure magic   pure ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>ian  enchantment  trapdoor to imaginary world...</td>\n",
       "      <td>0.002169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>harry potter and the sorcerer stone   harry ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
       "      <td>great journey to the magic world   great jou...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>487</td>\n",
       "      <td>I'm Sorry, Hermoine and Ron Who?\\n  I'm Sorry...</td>\n",
       "      <td>i am sorry  hermoine and ron who    i am sor...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>488</td>\n",
       "      <td>A fantastic ending\\n  A fantastic ending\\n  A...</td>\n",
       "      <td>a fantastic ending   a fantastic ending   a ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>489</td>\n",
       "      <td>LEGENDARY!\\n  LEGENDARY!\\n  LEGENDARY!\\n  LEG...</td>\n",
       "      <td>legendary legendary legendary legendary  lege...</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>Good but ending changed for worse\\n  Good but...</td>\n",
       "      <td>good but ending changed for worse   good but...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>Great conclusion, of fantastic story\\n  Great...</td>\n",
       "      <td>awesome  great conclusion  of fantastic story...</td>\n",
       "      <td>0.005618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>492 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "                                                review  \\\n",
       "0     The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1     Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2     Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3     Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4     Great Journey to the Magic World\\n  Great Jou...   \n",
       "..                                                 ...   \n",
       "487   I'm Sorry, Hermoine and Ron Who?\\n  I'm Sorry...   \n",
       "488   A fantastic ending\\n  A fantastic ending\\n  A...   \n",
       "489   LEGENDARY!\\n  LEGENDARY!\\n  LEGENDARY!\\n  LEG...   \n",
       "490   Good but ending changed for worse\\n  Good but...   \n",
       "491   Great conclusion, of fantastic story\\n  Great...   \n",
       "\n",
       "                                            new_review  percent_cap  \n",
       "0      the magic comes to life    the magic comes t...     0.000000  \n",
       "1      pure magic   pure magic   pure magic   pure ...     0.000000  \n",
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       "491   awesome  great conclusion  of fantastic story...     0.005618  \n",
       "\n",
       "[492 rows x 3 columns]"
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     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "positive_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"new_review\"] = negative_df[\"new_review\"].str.replace(r\"[^\\w^\\s]\", \" \")\n",
    "negative_df[\"new_review\"] = negative_df[\"new_review\"].str.replace('\\n',\" \") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
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       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
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       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "5   It's bland like drinking warm water\\n  It's b...   \n",
       "6   Harry Potter and the Chamber of Commerce\\n  H...   \n",
       "7   its not worth the piece of paper the ticket w...   \n",
       "8   My Revenge for the Non Fans!!\\n  My Revenge f...   \n",
       "9   Terrible Terrible film\\n  Terrible Terrible f...   \n",
       "\n",
       "                                          new_review  percent_cap  \n",
       "0   slight spoilers  for kids only  unfortunately...     0.010152  \n",
       "1    an effects laden excuse of an adaptation   a...     0.000000  \n",
       "2   wow had ringwraiths entz shelob yeah  hollywo...     0.010033  \n",
       "3   jrr  dismal  contrived  ripoff and just plain...     0.005291  \n",
       "4   spoilers lot  great storytelling  no story   ...     0.003268  \n",
       "5    it bland like drinking warm water   it bland...     0.000000  \n",
       "6    harry potter and the chamber of commerce   h...     0.000000  \n",
       "7    its not worth the piece of paper the ticket ...     0.000000  \n",
       "8   aol  my revenge for the non fans     my reven...     0.002725  \n",
       "9    terrible terrible film   terrible terrible f...     0.000000  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing any word that contains less than 3 characters \n",
    "positive_df[\"new_review\"] = positive_df.apply(lambda row: remove_words_less_than_3_characters(row[\"new_review\"]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"new_review\"] = negative_df.apply(lambda row: remove_words_less_than_3_characters(row[\"new_review\"]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
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       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
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       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "5   The beginning of a magical journey\\n  The beg...   \n",
       "6   Very Book Accurate and Enjoyable!!\\n  Very Bo...   \n",
       "7   Great\\n  Great\\n  Great\\n  Great\\n  It brough...   \n",
       "8   One of my favorites\\n  One of my favorites\\n ...   \n",
       "9   Absolutely amazing\\n  Absolutely amazing\\n  A...   \n",
       "\n",
       "                                          new_review  percent_cap  \n",
       "0   the magic comes life the magic comes life the...     0.000000  \n",
       "1   pure magic pure magic pure magic pure magic t...     0.000000  \n",
       "2   ian enchantment trapdoor imaginary world ench...     0.002169  \n",
       "3   harry potter and the sorcerer stone harry pot...     0.000000  \n",
       "4   great journey the magic world great journey t...     0.000000  \n",
       "5   the beginning magical journey the beginning m...     0.000000  \n",
       "6   very loves very book accurate and enjoyable v...     0.017857  \n",
       "7   great great great great brought out almost ev...     0.000000  \n",
       "8   jkr one favorites one favorites one favorites...     0.012658  \n",
       "9   absolutely amazing absolutely amazing absolut...     0.000000  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "positive_df.head(10)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9</td>\n",
       "      <td>Terrible Terrible film\\n  Terrible Terrible f...</td>\n",
       "      <td>terrible terrible film terrible terrible film...</td>\n",
       "      <td>0.000000</td>\n",
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       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb...   \n",
       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "5   It's bland like drinking warm water\\n  It's b...   \n",
       "6   Harry Potter and the Chamber of Commerce\\n  H...   \n",
       "7   its not worth the piece of paper the ticket w...   \n",
       "8   My Revenge for the Non Fans!!\\n  My Revenge f...   \n",
       "9   Terrible Terrible film\\n  Terrible Terrible f...   \n",
       "\n",
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       "1   effects laden excuse adaptation effects laden...     0.000000  \n",
       "2   wow had ringwraiths entz shelob yeah hollywoo...     0.010033  \n",
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       "7   its not worth the piece paper the ticket was ...     0.000000  \n",
       "8   aol revenge for the non fans revenge for the ...     0.002725  \n",
       "9   terrible terrible film terrible terrible film...     0.000000  "
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     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Need to tokenize the reviews "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df[\"review_tokenize\"] = positive_df.apply(lambda row: nltk.word_tokenize(row[\"new_review\"]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"review_tokenize\"] = negative_df.apply(lambda row: nltk.word_tokenize(row[\"new_review\"]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
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       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "\n",
       "                                          new_review  percent_cap  \\\n",
       "0   the magic comes life the magic comes life the...     0.000000   \n",
       "1   pure magic pure magic pure magic pure magic t...     0.000000   \n",
       "2   ian enchantment trapdoor imaginary world ench...     0.002169   \n",
       "3   harry potter and the sorcerer stone harry pot...     0.000000   \n",
       "4   great journey the magic world great journey t...     0.000000   \n",
       "\n",
       "                                     review_tokenize  \n",
       "0  [the, magic, comes, life, the, magic, comes, l...  \n",
       "1  [pure, magic, pure, magic, pure, magic, pure, ...  \n",
       "2  [ian, enchantment, trapdoor, imaginary, world,...  \n",
       "3  [harry, potter, and, the, sorcerer, stone, har...  \n",
       "4  [great, journey, the, magic, world, great, jou...  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>percent_cap</th>\n",
       "      <th>review_tokenize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>For Kids Only, Unfortunately\\n  For Kids Only...</td>\n",
       "      <td>slight spoilers for kids only unfortunately f...</td>\n",
       "      <td>0.010152</td>\n",
       "      <td>[slight, spoilers, for, kids, only, unfortunat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>An effects-laden excuse of an adaptation\\n  A...</td>\n",
       "      <td>effects laden excuse adaptation effects laden...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[effects, laden, excuse, adaptation, effects, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Hollywood's greatest shame.\\n  Hollywood's gr...</td>\n",
       "      <td>wow had ringwraiths entz shelob yeah hollywoo...</td>\n",
       "      <td>0.010033</td>\n",
       "      <td>[wow, had, ringwraiths, entz, shelob, yeah, ho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dismal, Contrived, Ripoff and just plain dumb...</td>\n",
       "      <td>jrr dismal contrived ripoff and just plain du...</td>\n",
       "      <td>0.005291</td>\n",
       "      <td>[jrr, dismal, contrived, ripoff, and, just, pl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great storytelling, no story\\n  Great storyte...</td>\n",
       "      <td>spoilers lot great storytelling story great s...</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>[spoilers, lot, great, storytelling, story, gr...</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb...   \n",
       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "\n",
       "                                          new_review  percent_cap  \\\n",
       "0   slight spoilers for kids only unfortunately f...     0.010152   \n",
       "1   effects laden excuse adaptation effects laden...     0.000000   \n",
       "2   wow had ringwraiths entz shelob yeah hollywoo...     0.010033   \n",
       "3   jrr dismal contrived ripoff and just plain du...     0.005291   \n",
       "4   spoilers lot great storytelling story great s...     0.003268   \n",
       "\n",
       "                                     review_tokenize  \n",
       "0  [slight, spoilers, for, kids, only, unfortunat...  \n",
       "1  [effects, laden, excuse, adaptation, effects, ...  \n",
       "2  [wow, had, ringwraiths, entz, shelob, yeah, ho...  \n",
       "3  [jrr, dismal, contrived, ripoff, and, just, pl...  \n",
       "4  [spoilers, lot, great, storytelling, story, gr...  "
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Writing the pandas df to a csv file to save what I have done. \n",
    "positive_df.to_csv(r'positive_hp_reviews.csv')\n",
    "negative_df.to_csv(r'negative_hp_reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, I am going to remove a selected list of stopwords from the tokenized review\n",
    "stopwords = [\"the\", \"and\", \"was\", \"that\", \"this\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing stopwords in the tokenized review \n",
    "positive_df[\"stopwords_removed\"] = positive_df[\"review_tokenize\"].apply(lambda row: stop_word_removal(stopwords, row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>percent_cap</th>\n",
       "      <th>review_tokenize</th>\n",
       "      <th>stopwords_removed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "      <td>the magic comes life the magic comes life the...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[the, magic, comes, life, the, magic, comes, l...</td>\n",
       "      <td>[magic, comes, life, magic, comes, life, magic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "      <td>pure magic pure magic pure magic pure magic t...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[pure, magic, pure, magic, pure, magic, pure, ...</td>\n",
       "      <td>[pure, magic, pure, magic, pure, magic, pure, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>ian enchantment trapdoor imaginary world ench...</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>[ian, enchantment, trapdoor, imaginary, world,...</td>\n",
       "      <td>[ian, enchantment, trapdoor, imaginary, world,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>harry potter and the sorcerer stone harry pot...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[harry, potter, and, the, sorcerer, stone, har...</td>\n",
       "      <td>[harry, potter, sorcerer, stone, harry, potter...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
       "      <td>great journey the magic world great journey t...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[great, journey, the, magic, world, great, jou...</td>\n",
       "      <td>[great, journey, magic, world, great, journey,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "\n",
       "                                          new_review  percent_cap  \\\n",
       "0   the magic comes life the magic comes life the...     0.000000   \n",
       "1   pure magic pure magic pure magic pure magic t...     0.000000   \n",
       "2   ian enchantment trapdoor imaginary world ench...     0.002169   \n",
       "3   harry potter and the sorcerer stone harry pot...     0.000000   \n",
       "4   great journey the magic world great journey t...     0.000000   \n",
       "\n",
       "                                     review_tokenize  \\\n",
       "0  [the, magic, comes, life, the, magic, comes, l...   \n",
       "1  [pure, magic, pure, magic, pure, magic, pure, ...   \n",
       "2  [ian, enchantment, trapdoor, imaginary, world,...   \n",
       "3  [harry, potter, and, the, sorcerer, stone, har...   \n",
       "4  [great, journey, the, magic, world, great, jou...   \n",
       "\n",
       "                                   stopwords_removed  \n",
       "0  [magic, comes, life, magic, comes, life, magic...  \n",
       "1  [pure, magic, pure, magic, pure, magic, pure, ...  \n",
       "2  [ian, enchantment, trapdoor, imaginary, world,...  \n",
       "3  [harry, potter, sorcerer, stone, harry, potter...  \n",
       "4  [great, journey, magic, world, great, journey,...  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_df[\"stopwords_removed\"] = negative_df[\"review_tokenize\"].apply(lambda row: stop_word_removal(stopwords, row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>percent_cap</th>\n",
       "      <th>review_tokenize</th>\n",
       "      <th>stopwords_removed</th>\n",
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       "      <td>For Kids Only, Unfortunately\\n  For Kids Only...</td>\n",
       "      <td>slight spoilers for kids only unfortunately f...</td>\n",
       "      <td>0.010152</td>\n",
       "      <td>[slight, spoilers, for, kids, only, unfortunat...</td>\n",
       "      <td>[slight, spoilers, for, kids, only, unfortunat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>An effects-laden excuse of an adaptation\\n  A...</td>\n",
       "      <td>effects laden excuse adaptation effects laden...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[effects, laden, excuse, adaptation, effects, ...</td>\n",
       "      <td>[effects, laden, excuse, adaptation, effects, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Hollywood's greatest shame.\\n  Hollywood's gr...</td>\n",
       "      <td>wow had ringwraiths entz shelob yeah hollywoo...</td>\n",
       "      <td>0.010033</td>\n",
       "      <td>[wow, had, ringwraiths, entz, shelob, yeah, ho...</td>\n",
       "      <td>[wow, had, ringwraiths, entz, shelob, yeah, ho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dismal, Contrived, Ripoff and just plain dumb...</td>\n",
       "      <td>jrr dismal contrived ripoff and just plain du...</td>\n",
       "      <td>0.005291</td>\n",
       "      <td>[jrr, dismal, contrived, ripoff, and, just, pl...</td>\n",
       "      <td>[jrr, dismal, contrived, ripoff, just, plain, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great storytelling, no story\\n  Great storyte...</td>\n",
       "      <td>spoilers lot great storytelling story great s...</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>[spoilers, lot, great, storytelling, story, gr...</td>\n",
       "      <td>[spoilers, lot, great, storytelling, story, gr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb...   \n",
       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "\n",
       "                                          new_review  percent_cap  \\\n",
       "0   slight spoilers for kids only unfortunately f...     0.010152   \n",
       "1   effects laden excuse adaptation effects laden...     0.000000   \n",
       "2   wow had ringwraiths entz shelob yeah hollywoo...     0.010033   \n",
       "3   jrr dismal contrived ripoff and just plain du...     0.005291   \n",
       "4   spoilers lot great storytelling story great s...     0.003268   \n",
       "\n",
       "                                     review_tokenize  \\\n",
       "0  [slight, spoilers, for, kids, only, unfortunat...   \n",
       "1  [effects, laden, excuse, adaptation, effects, ...   \n",
       "2  [wow, had, ringwraiths, entz, shelob, yeah, ho...   \n",
       "3  [jrr, dismal, contrived, ripoff, and, just, pl...   \n",
       "4  [spoilers, lot, great, storytelling, story, gr...   \n",
       "\n",
       "                                   stopwords_removed  \n",
       "0  [slight, spoilers, for, kids, only, unfortunat...  \n",
       "1  [effects, laden, excuse, adaptation, effects, ...  \n",
       "2  [wow, had, ringwraiths, entz, shelob, yeah, ho...  \n",
       "3  [jrr, dismal, contrived, ripoff, just, plain, ...  \n",
       "4  [spoilers, lot, great, storytelling, story, gr...  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Writing the pandas df to a csv file to save what I have done. \n",
    "positive_df.to_csv(r'positive_hp_reviews.csv')\n",
    "negative_df.to_csv(r'negative_hp_reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, I am going to visualize the stopwords_removed reviews and decide on what the next processing steps are. \n",
    "#Visualizing the data without the stopwords \n",
    "pos_viz = pd.DataFrame()\n",
    "pos_viz[\"stopwords_removed\"] = positive_df[\"stopwords_removed\"].copy()\n",
    "pos_viz[\"stopwords_removed\"] = getting_data_ready_for_freq(pos_viz, \"stopwords_removed\")\n",
    "stopwords_removed_dict_pos = creating_freq_list_from_df_to_dict(pos_viz, \"stopwords_removed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_viz = pd.DataFrame()\n",
    "neg_viz[\"stopwords_removed\"] = negative_df[\"stopwords_removed\"].copy()\n",
    "neg_viz[\"stopwords_removed\"] = getting_data_ready_for_freq(neg_viz, \"stopwords_removed\")\n",
    "stopwords_removed_dict_neg = creating_freq_list_from_df_to_dict(neg_viz, \"stopwords_removed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Going to remove one more set of stopwords\n",
    "stopwords = [\"movie\", \"for\", \"they\", \"with\", \"have\", \"film\", \"all\", \"you\", \"are\", \"just\", \"there\", \"one\", \"what\", \"has\", \n",
    "            \"his\", \"her\", \"your\", \"mine\", \"from\", \"not\", \"but\", \"like\", \"harry\", \"will\", \"good\", \"time\", \"will\", \"really\", \"story\", \"who\"]\n",
    "negative_df[\"stopwords_removed\"] = negative_df[\"stopwords_removed\"].apply(lambda row: stop_word_removal(stopwords, row))\n",
    "positive_df[\"stopwords_removed\"] = positive_df[\"stopwords_removed\"].apply(lambda row: stop_word_removal(stopwords, row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>percent_cap</th>\n",
       "      <th>review_tokenize</th>\n",
       "      <th>stopwords_removed</th>\n",
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       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "      <td>the magic comes life the magic comes life the...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[the, magic, comes, life, the, magic, comes, l...</td>\n",
       "      <td>[magic, comes, life, magic, comes, life, magic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "      <td>pure magic pure magic pure magic pure magic t...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[pure, magic, pure, magic, pure, magic, pure, ...</td>\n",
       "      <td>[pure, magic, pure, magic, pure, magic, pure, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>ian enchantment trapdoor imaginary world ench...</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>[ian, enchantment, trapdoor, imaginary, world,...</td>\n",
       "      <td>[ian, enchantment, trapdoor, imaginary, world,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>harry potter and the sorcerer stone harry pot...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[harry, potter, and, the, sorcerer, stone, har...</td>\n",
       "      <td>[potter, sorcerer, stone, potter, sorcerer, st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
       "      <td>great journey the magic world great journey t...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[great, journey, the, magic, world, great, jou...</td>\n",
       "      <td>[great, journey, magic, world, great, journey,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "\n",
       "                                          new_review  percent_cap  \\\n",
       "0   the magic comes life the magic comes life the...     0.000000   \n",
       "1   pure magic pure magic pure magic pure magic t...     0.000000   \n",
       "2   ian enchantment trapdoor imaginary world ench...     0.002169   \n",
       "3   harry potter and the sorcerer stone harry pot...     0.000000   \n",
       "4   great journey the magic world great journey t...     0.000000   \n",
       "\n",
       "                                     review_tokenize  \\\n",
       "0  [the, magic, comes, life, the, magic, comes, l...   \n",
       "1  [pure, magic, pure, magic, pure, magic, pure, ...   \n",
       "2  [ian, enchantment, trapdoor, imaginary, world,...   \n",
       "3  [harry, potter, and, the, sorcerer, stone, har...   \n",
       "4  [great, journey, the, magic, world, great, jou...   \n",
       "\n",
       "                                   stopwords_removed  \n",
       "0  [magic, comes, life, magic, comes, life, magic...  \n",
       "1  [pure, magic, pure, magic, pure, magic, pure, ...  \n",
       "2  [ian, enchantment, trapdoor, imaginary, world,...  \n",
       "3  [potter, sorcerer, stone, potter, sorcerer, st...  \n",
       "4  [great, journey, magic, world, great, journey,...  "
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>percent_cap</th>\n",
       "      <th>review_tokenize</th>\n",
       "      <th>stopwords_removed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>For Kids Only, Unfortunately\\n  For Kids Only...</td>\n",
       "      <td>slight spoilers for kids only unfortunately f...</td>\n",
       "      <td>0.010152</td>\n",
       "      <td>[slight, spoilers, for, kids, only, unfortunat...</td>\n",
       "      <td>[slight, spoilers, kids, only, unfortunately, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>An effects-laden excuse of an adaptation\\n  A...</td>\n",
       "      <td>effects laden excuse adaptation effects laden...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[effects, laden, excuse, adaptation, effects, ...</td>\n",
       "      <td>[effects, laden, excuse, adaptation, effects, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Hollywood's greatest shame.\\n  Hollywood's gr...</td>\n",
       "      <td>wow had ringwraiths entz shelob yeah hollywoo...</td>\n",
       "      <td>0.010033</td>\n",
       "      <td>[wow, had, ringwraiths, entz, shelob, yeah, ho...</td>\n",
       "      <td>[wow, had, ringwraiths, entz, shelob, yeah, ho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dismal, Contrived, Ripoff and just plain dumb...</td>\n",
       "      <td>jrr dismal contrived ripoff and just plain du...</td>\n",
       "      <td>0.005291</td>\n",
       "      <td>[jrr, dismal, contrived, ripoff, and, just, pl...</td>\n",
       "      <td>[jrr, dismal, contrived, ripoff, plain, dumb, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great storytelling, no story\\n  Great storyte...</td>\n",
       "      <td>spoilers lot great storytelling story great s...</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>[spoilers, lot, great, storytelling, story, gr...</td>\n",
       "      <td>[spoilers, lot, great, storytelling, great, st...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   For Kids Only, Unfortunately\\n  For Kids Only...   \n",
       "1   An effects-laden excuse of an adaptation\\n  A...   \n",
       "2   Hollywood's greatest shame.\\n  Hollywood's gr...   \n",
       "3   Dismal, Contrived, Ripoff and just plain dumb...   \n",
       "4   Great storytelling, no story\\n  Great storyte...   \n",
       "\n",
       "                                          new_review  percent_cap  \\\n",
       "0   slight spoilers for kids only unfortunately f...     0.010152   \n",
       "1   effects laden excuse adaptation effects laden...     0.000000   \n",
       "2   wow had ringwraiths entz shelob yeah hollywoo...     0.010033   \n",
       "3   jrr dismal contrived ripoff and just plain du...     0.005291   \n",
       "4   spoilers lot great storytelling story great s...     0.003268   \n",
       "\n",
       "                                     review_tokenize  \\\n",
       "0  [slight, spoilers, for, kids, only, unfortunat...   \n",
       "1  [effects, laden, excuse, adaptation, effects, ...   \n",
       "2  [wow, had, ringwraiths, entz, shelob, yeah, ho...   \n",
       "3  [jrr, dismal, contrived, ripoff, and, just, pl...   \n",
       "4  [spoilers, lot, great, storytelling, story, gr...   \n",
       "\n",
       "                                   stopwords_removed  \n",
       "0  [slight, spoilers, kids, only, unfortunately, ...  \n",
       "1  [effects, laden, excuse, adaptation, effects, ...  \n",
       "2  [wow, had, ringwraiths, entz, shelob, yeah, ho...  \n",
       "3  [jrr, dismal, contrived, ripoff, plain, dumb, ...  \n",
       "4  [spoilers, lot, great, storytelling, great, st...  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, I am going to visualize the stopwords_removed reviews and decide on what the next processing steps are. \n",
    "#Visualizing the data without the stopwords \n",
    "pos_viz = pd.DataFrame()\n",
    "pos_viz[\"stopwords_removed\"] = positive_df[\"stopwords_removed\"].copy()\n",
    "pos_viz[\"stopwords_removed\"] = getting_data_ready_for_freq(pos_viz, \"stopwords_removed\")\n",
    "stopwords_removed_dict_pos = creating_freq_list_from_df_to_dict(pos_viz, \"stopwords_removed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_viz = pd.DataFrame()\n",
    "neg_viz[\"stopwords_removed\"] = negative_df[\"stopwords_removed\"].copy()\n",
    "neg_viz[\"stopwords_removed\"] = getting_data_ready_for_freq(neg_viz, \"stopwords_removed\")\n",
    "stopwords_removed_dict_neg = creating_freq_list_from_df_to_dict(neg_viz, \"stopwords_removed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mask from: https://www.seekpng.com/ipng/u2r5w7e6r5q8e6t4_harry-potter-silhouette-clipart-harry-potter-and-the/\n",
    "#creating an array of arrays for the mask \n",
    "positive_word_cloud = create_word_cloud_with_mask(\"snape.PNG\", stopwords_removed_dict_pos, 750, \"Positive Review Word Cloud\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mask from https://www.seekpng.com/ipng/u2r5w7e6r5q8e6t4_harry-potter-silhouette-clipart-harry-potter-and-the/\n",
    "#creating an array of arrays for the mask \n",
    "negative_word_cloud = create_word_cloud_with_mask(\"snape.PNG\", stopwords_removed_dict_neg, 750, \"Negative Review Word Cloud\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1211</td>\n",
       "      <td>potter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>760</td>\n",
       "      <td>book</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>659</td>\n",
       "      <td>best</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>599</td>\n",
       "      <td>more</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>531</td>\n",
       "      <td>great</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>495</td>\n",
       "      <td>very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>413</td>\n",
       "      <td>488</td>\n",
       "      <td>first</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>511</td>\n",
       "      <td>471</td>\n",
       "      <td>series</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>437</td>\n",
       "      <td>out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>417</td>\n",
       "      <td>some</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>394</td>\n",
       "      <td>much</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>387</td>\n",
       "      <td>well</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>385</td>\n",
       "      <td>than</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>380</td>\n",
       "      <td>were</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218</td>\n",
       "      <td>364</td>\n",
       "      <td>part</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>774</td>\n",
       "      <td>357</td>\n",
       "      <td>films</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>499</td>\n",
       "      <td>351</td>\n",
       "      <td>see</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>206</td>\n",
       "      <td>343</td>\n",
       "      <td>about</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>341</td>\n",
       "      <td>when</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>236</td>\n",
       "      <td>340</td>\n",
       "      <td>also</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count    word\n",
       "44    1211  potter\n",
       "147    760    book\n",
       "210    659    best\n",
       "144    599    more\n",
       "156    531   great\n",
       "175    495    very\n",
       "413    488   first\n",
       "511    471  series\n",
       "94     437     out\n",
       "97     417    some\n",
       "143    394    much\n",
       "77     387    well\n",
       "145    385    than\n",
       "119    380    were\n",
       "218    364    part\n",
       "774    357   films\n",
       "499    351     see\n",
       "206    343   about\n",
       "89     341    when\n",
       "236    340    also"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizing the top 12 words/characters\n",
    "eda_reviews_top_words_pos = word_freq_dict_to_df_top_words(stopwords_removed_dict_pos, 20)\n",
    "eda_reviews_top_words_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive_bar_plot = top_words_bar_plot(eda_reviews_top_words_pos, \"Top 20 Positive Words\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>1208</td>\n",
       "      <td>book</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>908</td>\n",
       "      <td>potter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>645</td>\n",
       "      <td>out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>555</td>\n",
       "      <td>were</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>514</td>\n",
       "      <td>books</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>499</td>\n",
       "      <td>would</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>486</td>\n",
       "      <td>about</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>480</td>\n",
       "      <td>read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>460</td>\n",
       "      <td>much</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>444</td>\n",
       "      <td>more</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>441</td>\n",
       "      <td>how</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>431</td>\n",
       "      <td>even</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>419</td>\n",
       "      <td>movies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>392</td>\n",
       "      <td>when</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>391</td>\n",
       "      <td>been</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>386</td>\n",
       "      <td>had</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>386</td>\n",
       "      <td>first</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>372</td>\n",
       "      <td>only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>327</td>\n",
       "      <td>357</td>\n",
       "      <td>see</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213</td>\n",
       "      <td>355</td>\n",
       "      <td>did</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count    word\n",
       "108   1208    book\n",
       "87     908  potter\n",
       "38     645     out\n",
       "10     555    were\n",
       "97     514   books\n",
       "41     499   would\n",
       "45     486   about\n",
       "200    480    read\n",
       "53     460    much\n",
       "80     444    more\n",
       "164    441     how\n",
       "79     431    even\n",
       "204    419  movies\n",
       "167    392    when\n",
       "42     391    been\n",
       "56     386     had\n",
       "180    386   first\n",
       "3      372    only\n",
       "327    357     see\n",
       "213    355     did"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizing the top 12 words/characters\n",
    "eda_reviews_top_words_neg = word_freq_dict_to_df_top_words(stopwords_removed_dict_neg, 20)\n",
    "eda_reviews_top_words_neg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "negative_bar_plot = top_words_bar_plot(eda_reviews_top_words_neg, \"Top 20 Negative Words\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Positive *****\n",
      "The total number of words is 88956\n",
      "The total number of unique words is 9050\n",
      "***** Negative *****\n",
      "The total number of words is 89101\n",
      "The total number of unique words is 9175\n"
     ]
    }
   ],
   "source": [
    "print(\"***** Positive *****\")\n",
    "total_words_unique_words(stopwords_removed_dict_pos)\n",
    "print(\"***** Negative *****\")\n",
    "total_words_unique_words(stopwords_removed_dict_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding a label to the positive reviews and negative reviews \n",
    "positive_df[\"label\"] = \"pos\"\n",
    "negative_df[\"label\"] = \"neg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combining the positive and negative df together \n",
    "combined_df = pd.concat([positive_df, negative_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>percent_cap</th>\n",
       "      <th>review_tokenize</th>\n",
       "      <th>stopwords_removed</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "      <td>the magic comes life the magic comes life the...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[the, magic, comes, life, the, magic, comes, l...</td>\n",
       "      <td>[magic, comes, life, magic, comes, life, magic...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "      <td>pure magic pure magic pure magic pure magic t...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[pure, magic, pure, magic, pure, magic, pure, ...</td>\n",
       "      <td>[pure, magic, pure, magic, pure, magic, pure, ...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>ian enchantment trapdoor imaginary world ench...</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>[ian, enchantment, trapdoor, imaginary, world,...</td>\n",
       "      <td>[ian, enchantment, trapdoor, imaginary, world,...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>harry potter and the sorcerer stone harry pot...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[harry, potter, and, the, sorcerer, stone, har...</td>\n",
       "      <td>[potter, sorcerer, stone, potter, sorcerer, st...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
       "      <td>great journey the magic world great journey t...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[great, journey, the, magic, world, great, jou...</td>\n",
       "      <td>[great, journey, magic, world, great, journey,...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "\n",
       "                                          new_review  percent_cap  \\\n",
       "0   the magic comes life the magic comes life the...     0.000000   \n",
       "1   pure magic pure magic pure magic pure magic t...     0.000000   \n",
       "2   ian enchantment trapdoor imaginary world ench...     0.002169   \n",
       "3   harry potter and the sorcerer stone harry pot...     0.000000   \n",
       "4   great journey the magic world great journey t...     0.000000   \n",
       "\n",
       "                                     review_tokenize  \\\n",
       "0  [the, magic, comes, life, the, magic, comes, l...   \n",
       "1  [pure, magic, pure, magic, pure, magic, pure, ...   \n",
       "2  [ian, enchantment, trapdoor, imaginary, world,...   \n",
       "3  [harry, potter, and, the, sorcerer, stone, har...   \n",
       "4  [great, journey, the, magic, world, great, jou...   \n",
       "\n",
       "                                   stopwords_removed label  \n",
       "0  [magic, comes, life, magic, comes, life, magic...   pos  \n",
       "1  [pure, magic, pure, magic, pure, magic, pure, ...   pos  \n",
       "2  [ian, enchantment, trapdoor, imaginary, world,...   pos  \n",
       "3  [potter, sorcerer, stone, potter, sorcerer, st...   pos  \n",
       "4  [great, journey, magic, world, great, journey,...   pos  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>review</th>\n",
       "      <th>new_review</th>\n",
       "      <th>percent_cap</th>\n",
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       "      <td>0</td>\n",
       "      <td>The Magic Comes To Life!\\n  The Magic Comes T...</td>\n",
       "      <td>the magic comes life the magic comes life the...</td>\n",
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       "      <td>pos</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...</td>\n",
       "      <td>pure magic pure magic pure magic pure magic t...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[pure, magic, pure, magic, pure, magic, pure, ...</td>\n",
       "      <td>[pure, magic, pure, magic, pure, magic, pure, ...</td>\n",
       "      <td>pos</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Enchantment, Trapdoor to Imaginary World.\\n  ...</td>\n",
       "      <td>ian enchantment trapdoor imaginary world ench...</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>[ian, enchantment, trapdoor, imaginary, world,...</td>\n",
       "      <td>[ian, enchantment, trapdoor, imaginary, world,...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone\\n  Harr...</td>\n",
       "      <td>harry potter and the sorcerer stone harry pot...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[harry, potter, and, the, sorcerer, stone, har...</td>\n",
       "      <td>[potter, sorcerer, stone, potter, sorcerer, st...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Great Journey to the Magic World\\n  Great Jou...</td>\n",
       "      <td>great journey the magic world great journey t...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[great, journey, the, magic, world, great, jou...</td>\n",
       "      <td>[great, journey, magic, world, great, journey,...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>979</td>\n",
       "      <td>487</td>\n",
       "      <td>Deathly Disappointing\\n  Deathly Disappointin...</td>\n",
       "      <td>owl rofle omg deathly disappointing deathly d...</td>\n",
       "      <td>0.005376</td>\n",
       "      <td>[owl, rofle, omg, deathly, disappointing, deat...</td>\n",
       "      <td>[owl, rofle, omg, deathly, disappointing, deat...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>980</td>\n",
       "      <td>488</td>\n",
       "      <td>Nothing but disappointing.\\n  Nothing but dis...</td>\n",
       "      <td>nothing but disappointing nothing but disappo...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[nothing, but, disappointing, nothing, but, di...</td>\n",
       "      <td>[nothing, disappointing, nothing, disappointin...</td>\n",
       "      <td>neg</td>\n",
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       "    <tr>\n",
       "      <td>981</td>\n",
       "      <td>489</td>\n",
       "      <td>A middling series ends on a low-point.\\n  A m...</td>\n",
       "      <td>middling series ends low point middling serie...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[middling, series, ends, low, point, middling,...</td>\n",
       "      <td>[middling, series, ends, low, point, middling,...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>982</td>\n",
       "      <td>490</td>\n",
       "      <td>OK but not I'm not Wowed!\\n  OK but not I'm n...</td>\n",
       "      <td>but not not wowed but not not wowed but not n...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[but, not, not, wowed, but, not, not, wowed, b...</td>\n",
       "      <td>[wowed, wowed, wowed, wowed, potter, deathly, ...</td>\n",
       "      <td>neg</td>\n",
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       "    <tr>\n",
       "      <td>983</td>\n",
       "      <td>491</td>\n",
       "      <td>Its OK, not great.\\n  Its OK, not great.\\n  I...</td>\n",
       "      <td>dvd its not great its not great its not great...</td>\n",
       "      <td>0.006410</td>\n",
       "      <td>[dvd, its, not, great, its, not, great, its, n...</td>\n",
       "      <td>[dvd, its, great, its, great, its, great, its,...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                             review  \\\n",
       "0        0   The Magic Comes To Life!\\n  The Magic Comes T...   \n",
       "1        1   Pure Magic\\n  Pure Magic\\n  Pure Magic\\n  Pur...   \n",
       "2        2   Enchantment, Trapdoor to Imaginary World.\\n  ...   \n",
       "3        3   Harry Potter and the Sorcerer's Stone\\n  Harr...   \n",
       "4        4   Great Journey to the Magic World\\n  Great Jou...   \n",
       "..     ...                                                ...   \n",
       "979    487   Deathly Disappointing\\n  Deathly Disappointin...   \n",
       "980    488   Nothing but disappointing.\\n  Nothing but dis...   \n",
       "981    489   A middling series ends on a low-point.\\n  A m...   \n",
       "982    490   OK but not I'm not Wowed!\\n  OK but not I'm n...   \n",
       "983    491   Its OK, not great.\\n  Its OK, not great.\\n  I...   \n",
       "\n",
       "                                            new_review  percent_cap  \\\n",
       "0     the magic comes life the magic comes life the...     0.000000   \n",
       "1     pure magic pure magic pure magic pure magic t...     0.000000   \n",
       "2     ian enchantment trapdoor imaginary world ench...     0.002169   \n",
       "3     harry potter and the sorcerer stone harry pot...     0.000000   \n",
       "4     great journey the magic world great journey t...     0.000000   \n",
       "..                                                 ...          ...   \n",
       "979   owl rofle omg deathly disappointing deathly d...     0.005376   \n",
       "980   nothing but disappointing nothing but disappo...     0.000000   \n",
       "981   middling series ends low point middling serie...     0.000000   \n",
       "982   but not not wowed but not not wowed but not n...     0.000000   \n",
       "983   dvd its not great its not great its not great...     0.006410   \n",
       "\n",
       "                                       review_tokenize  \\\n",
       "0    [the, magic, comes, life, the, magic, comes, l...   \n",
       "1    [pure, magic, pure, magic, pure, magic, pure, ...   \n",
       "2    [ian, enchantment, trapdoor, imaginary, world,...   \n",
       "3    [harry, potter, and, the, sorcerer, stone, har...   \n",
       "4    [great, journey, the, magic, world, great, jou...   \n",
       "..                                                 ...   \n",
       "979  [owl, rofle, omg, deathly, disappointing, deat...   \n",
       "980  [nothing, but, disappointing, nothing, but, di...   \n",
       "981  [middling, series, ends, low, point, middling,...   \n",
       "982  [but, not, not, wowed, but, not, not, wowed, b...   \n",
       "983  [dvd, its, not, great, its, not, great, its, n...   \n",
       "\n",
       "                                     stopwords_removed label  \n",
       "0    [magic, comes, life, magic, comes, life, magic...   pos  \n",
       "1    [pure, magic, pure, magic, pure, magic, pure, ...   pos  \n",
       "2    [ian, enchantment, trapdoor, imaginary, world,...   pos  \n",
       "3    [potter, sorcerer, stone, potter, sorcerer, st...   pos  \n",
       "4    [great, journey, magic, world, great, journey,...   pos  \n",
       "..                                                 ...   ...  \n",
       "979  [owl, rofle, omg, deathly, disappointing, deat...   neg  \n",
       "980  [nothing, disappointing, nothing, disappointin...   neg  \n",
       "981  [middling, series, ends, low, point, middling,...   neg  \n",
       "982  [wowed, wowed, wowed, wowed, potter, deathly, ...   neg  \n",
       "983  [dvd, its, great, its, great, its, great, its,...   neg  \n",
       "\n",
       "[984 rows x 7 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I need to reset the index \n",
    "combined_df.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ready to see if I can predict the review sentiment \n",
    "no_stopwords_df = pd.DataFrame() \n",
    "no_stopwords_df[\"review\"] = combined_df[\"stopwords_removed\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_stopwords_df[\"label\"] = combined_df[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <td>2</td>\n",
       "      <td>[ian, enchantment, trapdoor, imaginary, world,...</td>\n",
       "      <td>pos</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>[potter, sorcerer, stone, potter, sorcerer, st...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>[great, journey, magic, world, great, journey,...</td>\n",
       "      <td>pos</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review label\n",
       "0  [magic, comes, life, magic, comes, life, magic...   pos\n",
       "1  [pure, magic, pure, magic, pure, magic, pure, ...   pos\n",
       "2  [ian, enchantment, trapdoor, imaginary, world,...   pos\n",
       "3  [potter, sorcerer, stone, potter, sorcerer, st...   pos\n",
       "4  [great, journey, magic, world, great, journey,...   pos"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_stopwords_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now I am going to create my bag of words for the tokenized reviews \n",
    "#First, I must remove the labels that I just added... \n",
    "no_stopwords_label = no_stopwords_df[\"label\"]\n",
    "no_stopwords_df.drop(\"label\", axis = 1, inplace = True)\n",
    "no_stopwords_bow = bag_of_words(no_stopwords_df, \"review\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'potter': 4,\n",
       "         'sorcerer': 4,\n",
       "         'stone': 4,\n",
       "         'unforgettable': 1,\n",
       "         'start': 1,\n",
       "         'fantastic': 1,\n",
       "         'series': 1,\n",
       "         'career': 1,\n",
       "         'impeccable': 1,\n",
       "         'emma': 1,\n",
       "         'watson': 1,\n",
       "         'other': 1,\n",
       "         'kids': 1})"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_stopwords_bow[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_stopwords_df = bow_to_df(no_stopwords_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "<p>5 rows × 12948 columns</p>\n",
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      ],
      "text/plain": [
       "   magic  comes  life  once  upon  long  ago  vivid  fertile  imagination  \\\n",
       "0     11      4     8     1     2     4    1      1        1            3   \n",
       "1      5      0     0     0     0     0    0      0        0            0   \n",
       "2      0      0     5     0     0     0    0      0        0            0   \n",
       "3      0      0     0     0     0     0    0      0        0            0   \n",
       "4      5      0     0     0     0     0    0      0        0            0   \n",
       "\n",
       "   ...  peaks  payoffs  footnotes  wowed  lashed  sellout  wash  hairs  \\\n",
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       "3  ...      0        0          0      0       0        0     0      0   \n",
       "4  ...      0        0          0      0       0        0     0      0   \n",
       "\n",
       "   tingling  lump  \n",
       "0         0     0  \n",
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       "\n",
       "[5 rows x 12948 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_stopwords_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_stopwords_df = normalize_df(no_stopwords_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>magic</th>\n",
       "      <th>comes</th>\n",
       "      <th>life</th>\n",
       "      <th>once</th>\n",
       "      <th>upon</th>\n",
       "      <th>long</th>\n",
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       "      <th>imagination</th>\n",
       "      <th>...</th>\n",
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      "text/plain": [
       "      magic     comes      life      once      upon      long       ago  \\\n",
       "0  0.020147  0.007326  0.014652  0.001832  0.003663  0.007326  0.001832   \n",
       "1  0.083333  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
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       "4  0.092593  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "      vivid   fertile  imagination  ...  peaks  payoffs  footnotes  wowed  \\\n",
       "0  0.001832  0.001832     0.005495  ...    0.0      0.0        0.0    0.0   \n",
       "1  0.000000  0.000000     0.000000  ...    0.0      0.0        0.0    0.0   \n",
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       "4  0.000000  0.000000     0.000000  ...    0.0      0.0        0.0    0.0   \n",
       "\n",
       "   lashed  sellout  wash  hairs  tingling  lump  \n",
       "0     0.0      0.0   0.0    0.0       0.0   0.0  \n",
       "1     0.0      0.0   0.0    0.0       0.0   0.0  \n",
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       "3     0.0      0.0   0.0    0.0       0.0   0.0  \n",
       "4     0.0      0.0   0.0    0.0       0.0   0.0  \n",
       "\n",
       "[5 rows x 12948 columns]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_stopwords_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now I need to remove the total column for each df \n",
    "no_stopwords_df.drop(\"total\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a testing and training df for the normalized dfs \n",
    "no_stopwords_test_train = no_stopwords_df.copy()\n",
    "test_train_label = combined_df[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating 4 df: 1: the training df with label removed, 2: the testing df with label removed, 3: the training label, 4: testing label\n",
    "no_stopwords_train, no_stopwords_test, no_stopwords_train_label, no_stopwords_test_label = train_test_split(no_stopwords_test_train, test_train_label, test_size = .3, random_state = 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'pos': 148, 'neg': 148})\n"
     ]
    }
   ],
   "source": [
    "#Getting a count of positive and negative opinions in the test label \n",
    "print(Counter(no_stopwords_test_label))\n",
    "#There are roughly the same number of positive and negative reviews in the test and train set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 0.902027027027027\n",
      "[[136  12]\n",
      " [ 17 131]]\n",
      "The number of true negatives is:  136\n",
      "The number of false positives is:  12\n",
      "The number of false negatives is:  17\n",
      "The number of true positives is:  131\n"
     ]
    }
   ],
   "source": [
    "#Naive Bayes attempt Multinomial \n",
    "clf = MultinomialNB()\n",
    "clf.fit(no_stopwords_train, no_stopwords_train_label)\n",
    "test_predicted = clf.predict(no_stopwords_test)\n",
    "#Getting the accuracy for naive bayes \n",
    "accuracy = accuracy_score(no_stopwords_test_label, test_predicted, normalize = True)\n",
    "print(\"The accuracy is\", accuracy)\n",
    "cm = confusion_matrix(no_stopwords_test_label, test_predicted)\n",
    "# confusion_matrix_graph(cm, accuracy, \"NB Multinomial No Stopwords\")\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "print(cm)\n",
    "print(\"The number of true negatives is: \", tn)\n",
    "print(\"The number of false positives is: \", fp)\n",
    "print(\"The number of false negatives is: \", fn)\n",
    "print(\"The number of true positives is: \", tp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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