{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HW4 Deception (V2)\n", "## STEP 1: Get Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "def get_data(file, path):\n", " f=open(path+file)\n", " data = f.read()\n", " f.close()\n", " return data\n", " \n", "def get_data_from_files(path):\n", " results = [get_data(file, path) for file in os.listdir(path)]\n", " return results\n", "\n", "# pos = get_data_from_files('../pos_cornell//')\n", "# neg = get_data_from_files('../neg_cornell/')\n", "pos = get_data_from_files('../hw4_lie_false/')\n", "neg = get_data_from_files('../hw4_lie_true/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 1b -- turn it into neg/pos df then merge df" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0PoN
0?N
1Twin Trees Cicero NY HUGE salad bar and high q...N
2The worst restaurant that I have ever eaten in...N
3?N
4I have been to a Asian restaurant in New York ...N
.........
87Mikes Pizza High Point NY Service was very slo...P
88After I went shopping with some of my friend w...P
89I entered the restaurant and a waitress came b...P
90Carlos Plate Shack was the worst dining experi...P
91Olive Oil Garden was very disappointing. I exp...P
\n", "

92 rows × 2 columns

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" ], "text/plain": [ " 0 PoN\n", "0 ? N\n", "1 Twin Trees Cicero NY HUGE salad bar and high q... N\n", "2 The worst restaurant that I have ever eaten in... N\n", "3 ? N\n", "4 I have been to a Asian restaurant in New York ... N\n", ".. ... ..\n", "87 Mikes Pizza High Point NY Service was very slo... P\n", "88 After I went shopping with some of my friend w... P\n", "89 I entered the restaurant and a waitress came b... P\n", "90 Carlos Plate Shack was the worst dining experi... P\n", "91 Olive Oil Garden was very disappointing. I exp... P\n", "\n", "[92 rows x 2 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "neg_df = pd.DataFrame(neg)\n", "pos_df = pd.DataFrame(pos)\n", "pos_df['PoN'] = 'P'\n", "neg_df['PoN'] = 'N'\n", "all_df = neg_df.append(pos_df)\n", "all_df.reset_index(drop=True,inplace=True)\n", "all_df" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "from collections import Counter\n", "from nltk.sentiment.util import *\n", "from nltk.tokenize import casual_tokenize\n", "all_df['pos'] = all_df.apply(lambda x: nltk.pos_tag(Counter(casual_tokenize(x[0]))),axis=1)\n", "\n", "def get_pos_list(tuples):\n", "# print(tuples)\n", " return [t[1] for t in tuples]\n", " \n", "all_df['pos_count'] = all_df.apply(lambda x: get_pos_list(x['pos']),axis=1)\n", "all_df['pos_str'] = all_df.apply(lambda x: ' '.join(x['pos_count']),axis=1)\n", "\n", "# all_df['pos_no_sw'] = all_df.apply(lambda x: nltk.pos_tag(x['no_sw']),axis=1)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "mylist = all_df['pos_str'].tolist()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'. NNP NNP NNP NNP NNP NN NN CC JJ NN JJ NNS . DT NN VBZ RB JJ JJ JJ NNP NN TO VB DT JJ NN NN IN RB RB IN PRP VBP VBN IN DT JJ NNS DT JJ NN IN PRP$ NNS JJ VB NN NN DT JJS NN IN PRP VBP RB VBN IN VBZ RB DT NN VBD NNP CC NN . VBD DT NN NN VBD JJ NN WRB DT NN VBD PRP$ NN IN NN PRP VBD RB JJ : CC WRB VBD NN IN WDT VBD RB NN IN NN RB JJ TO IN NN VBD DT NN RB VBP VB NN FW VB DT RB IN VBG JJ NN . PRP VBP VBN TO DT JJ NN IN NNP NNP NN . DT NN VBZ VBN IN NNP CC NNP WRB VBP JJ NNS NN VBN NNP NN VBD JJ NN IN PRP IN NNP NN WDT VBD NN DT JJS NN PRP VBP VBN TO VBZ WRB VBD NNP IN PRP$ NNS DT NN RB RB JJ . NNP JJ VBD JJ RB RB . NN CC JJ WDT VBZ PRP VB VB IN RBR NNS RB VBG VBG IN PDT DT JJ IN VBG NN RB DT NN VBD RB JJ DT NNS IN PRP DT NN CC VBD RB . NN VBD JJ PRP VBD RB IN JJ NN NN VBD JJ NN NN DT NN IN NNP NNP VBD DT JJ NN . 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NN RB IN CD RB JJ NN DT NN JJ JJ NN CC NN NNS VBD RB VBG NN NNP NN IN VBG DT JJ NN NN IN CD NNS JJ VB JJ NNS VB PRP$ NN JJ . JJ JJ IN NN NNP RB RB JJ CC JJ NNP CD NNS IN PRP CC JJ NN NNP RB NN RB PRP RB VBD TO NNP IN NNP NNP VBP PRP$ NN . DT NN VBZ JJ . WRB VBD NN VBD IN EX VBD DT NN CC NN CC VBP DT NN NNP MD DT NNP JJR NN NNP DT NN IN NN VBP NNS MD RB VB IN JJ NNS RBR JJ NN PRP VBD TO DT NN VBN NNP CC VB JJ VB IN DT NN IN JJ NNS . MD VB NN VBD RB CC PRP VBZ IN NN RB RB VBZ VBN JJ NNP NN RB VB PRP$ NNS MD VB RB IN JJ PDT PRP$ RB JJ NNP DT NN VBZ JJ CC PRP RB VBD JJ NN . VBZ NNS RB VBP PRP VBP TO VB WRB IN DT NN IN NN NN NN IN NN IN PRP VBP CC EX RB NN IN NN NNS VBP VBG NN NNP NNP NNP NNP NNP NNP VBD RB JJ CC DT NN JJ . PRP MD VB PRP VBP IN JJS WRB TO VB JJ NN RB NNP JJ NNS IN NN NN CC DT NN MD VB VBG RB WRB IN PRP VBD VBG IN DT IN PRP$ NN PRP TO NNP NN IN NN . VBN NN IN CD DT NNS PRP VBD DT NN CC DT NN VBD IN IN VBG VBG JJ NN IN NN VBD RB JJ PRP . RB PRP RB VBD JJ CC DT CD RB VB PRP IN VBD RB TO VB NN NN NN NNP NN RB RB VBD RBS JJ IN PRP$ NN VBD DT NN NN VBD JJ NNS VBD VB JJ RB VBP RB DT VBZ NN WDT MD RB VB NNP NNP NNP VBD DT JJS VBG NN IN PRP$ NN . IN JJ NNP VBD TO VB IN NN RB JJ IN DT NN PRP VBD RP IN NNS PRP VBD DT JJ NNP NN CC NNS JJ VBD CC NN JJ NN ( NN RB VBD VBN ) RB PRP$ NN VBD RB NN RB VBP NNS VBN IN JJ NNP NNP NNP NNP DT VBD RP RB JJ CD NNS IN NNS VBP JJ CD VBD RP VBG RB NN NN WRB PRP PRP$ VBD NN RB VB IN PRP IN PRP VBD RB RB VBP VB DT NN NN PRP$ JJ JJ NNS JJ JJ NN NN NN JJ NNP NNP VBD RB JJ . PRP VBP JJ NN CC NN ( IN JJS . ) WRB VB RP TO VB DT NN NN PRP VBD PRP DT NN VBD DT NNS RB NNP'" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "' '.join(mylist)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }