{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pos = pd.read_csv('AMT_pos.csv')\n",
    "neg = pd.read_csv('AMT_neg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>HITId</th>\n",
       "      <th>HITTypeId</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Reward</th>\n",
       "      <th>CreationTime</th>\n",
       "      <th>MaxAssignments</th>\n",
       "      <th>RequesterAnnotation</th>\n",
       "      <th>AssignmentDurationInSeconds</th>\n",
       "      <th>...</th>\n",
       "      <th>RejectionTime</th>\n",
       "      <th>RequesterFeedback</th>\n",
       "      <th>WorkTimeInSeconds</th>\n",
       "      <th>LifetimeApprovalRate</th>\n",
       "      <th>Last30DaysApprovalRate</th>\n",
       "      <th>Last7DaysApprovalRate</th>\n",
       "      <th>Input.text</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Approve</th>\n",
       "      <th>Reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3VMV5CHJZ8F47P7CECH0H830NF4GTP</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:11:19 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821427;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>355</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>funny like a clown\\nGreetings again from the d...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3VMV5CHJZ8F47P7CECH0H830NF4GTP</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:11:19 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821427;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>487</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>funny like a clown\\nGreetings again from the d...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3VMV5CHJZ8F47P7CECH0H830NF4GTP</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:11:19 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821427;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1052</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>funny like a clown\\nGreetings again from the d...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>36GJS3V78VQATMB72I3WF2GRPDDJGN</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:11:19 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821427;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>Only certain people can relate\\nThis is a mov...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>36GJS3V78VQATMB72I3WF2GRPDDJGN</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:11:19 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821427;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>666</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>Only certain people can relate\\nThis is a mov...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId                       HITTypeId  \\\n",
       "0  3VMV5CHJZ8F47P7CECH0H830NF4GTP  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "1  3VMV5CHJZ8F47P7CECH0H830NF4GTP  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "2  3VMV5CHJZ8F47P7CECH0H830NF4GTP  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "3  36GJS3V78VQATMB72I3WF2GRPDDJGN  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "4  36GJS3V78VQATMB72I3WF2GRPDDJGN  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "\n",
       "                Title         Description         Keywords Reward  \\\n",
       "0  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "1  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "2  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "3  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "4  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "\n",
       "                   CreationTime  MaxAssignments  \\\n",
       "0  Fri Nov 01 12:11:19 PDT 2019               3   \n",
       "1  Fri Nov 01 12:11:19 PDT 2019               3   \n",
       "2  Fri Nov 01 12:11:19 PDT 2019               3   \n",
       "3  Fri Nov 01 12:11:19 PDT 2019               3   \n",
       "4  Fri Nov 01 12:11:19 PDT 2019               3   \n",
       "\n",
       "                                RequesterAnnotation  \\\n",
       "0  BatchId:3821427;OriginalHitTemplateId:928390909;   \n",
       "1  BatchId:3821427;OriginalHitTemplateId:928390909;   \n",
       "2  BatchId:3821427;OriginalHitTemplateId:928390909;   \n",
       "3  BatchId:3821427;OriginalHitTemplateId:928390909;   \n",
       "4  BatchId:3821427;OriginalHitTemplateId:928390909;   \n",
       "\n",
       "   AssignmentDurationInSeconds  ...  RejectionTime RequesterFeedback  \\\n",
       "0                        10800  ...            NaN               NaN   \n",
       "1                        10800  ...            NaN               NaN   \n",
       "2                        10800  ...            NaN               NaN   \n",
       "3                        10800  ...            NaN               NaN   \n",
       "4                        10800  ...            NaN               NaN   \n",
       "\n",
       "   WorkTimeInSeconds  LifetimeApprovalRate Last30DaysApprovalRate  \\\n",
       "0                355              0% (0/0)               0% (0/0)   \n",
       "1                487              0% (0/0)               0% (0/0)   \n",
       "2               1052              0% (0/0)               0% (0/0)   \n",
       "3                  4              0% (0/0)               0% (0/0)   \n",
       "4                666              0% (0/0)               0% (0/0)   \n",
       "\n",
       "  Last7DaysApprovalRate                                         Input.text  \\\n",
       "0              0% (0/0)  funny like a clown\\nGreetings again from the d...   \n",
       "1              0% (0/0)  funny like a clown\\nGreetings again from the d...   \n",
       "2              0% (0/0)  funny like a clown\\nGreetings again from the d...   \n",
       "3              0% (0/0)   Only certain people can relate\\nThis is a mov...   \n",
       "4              0% (0/0)   Only certain people can relate\\nThis is a mov...   \n",
       "\n",
       "  Answer.sentiment.label Approve Reject  \n",
       "0               Positive     NaN    NaN  \n",
       "1                Neutral     NaN    NaN  \n",
       "2               Positive     NaN    NaN  \n",
       "3               Negative     NaN    NaN  \n",
       "4               Positive     NaN    NaN  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HITId', 'HITTypeId', 'Title', 'Description', 'Keywords', 'Reward',\n",
       "       'CreationTime', 'MaxAssignments', 'RequesterAnnotation',\n",
       "       'AssignmentDurationInSeconds', 'AutoApprovalDelayInSeconds',\n",
       "       'Expiration', 'NumberOfSimilarHITs', 'LifetimeInSeconds',\n",
       "       'AssignmentId', 'WorkerId', 'AssignmentStatus', 'AcceptTime',\n",
       "       'SubmitTime', 'AutoApprovalTime', 'ApprovalTime', 'RejectionTime',\n",
       "       'RequesterFeedback', 'WorkTimeInSeconds', 'LifetimeApprovalRate',\n",
       "       'Last30DaysApprovalRate', 'Last7DaysApprovalRate', 'Input.text',\n",
       "       'Answer.sentiment.label', 'Approve', 'Reject'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_turkers_pos = np.unique(pos['WorkerId'], return_counts=True)\n",
    "print(len(unique_turkers_pos[0]))\n",
    "turker_df = pd.DataFrame(zip(unique_turkers_pos[0], unique_turkers_pos[1]))\n",
    "sorted(turker_df)\n",
    "df = pd.DataFrame(turker_df.sort_values(by=1, ascending=False))\n",
    "# plt.plot(df)\n",
    "# plt.show()\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_turkers_neg = np.unique(neg['WorkerId'], return_counts=True)\n",
    "len(unique_turkers_neg[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_workers = pos.groupby('WorkerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pos_workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "bot = neg[neg['WorkerId'] == 'A3HAEQW13YPT6A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x11017b048>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for_plot = np.unique(bot['Answer.sentiment.label'].tolist(), return_counts=True)\n",
    "for_plot = zip(for_plot[0], for_plot[1])\n",
    "for_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 795.725x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "def make_plot(x, y, col, color, size, data):\n",
    "    sns.set()\n",
    "    sns.relplot(x=x, y=y, col=col, hue=color, size=size, data=data)\n",
    "\n",
    "data = sns.load_dataset(\"tips\")\n",
    "make_plot('total_bill', 'tip', 'time', 'smoker', 'size', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e49c048>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make_plot('total_bill', 'tip', 'time', 'smoker', 'size', pos)\n",
    "\n",
    "# sns.scatterplot(x=\"total_bill\", y=\"tip\", hue=\"day\", data=tips, ax=axes[1]);\n",
    "\n",
    "sns.boxplot(x=\"Answer.sentiment.label\", y=\"WorkTimeInSeconds\", data=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Answer.sentiment.label\", y=\"WorkTimeInSeconds\", kind=\"bar\", data=pos);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Answer.sentiment.label\", y=\"WorkTimeInSeconds\", kind=\"bar\", data=neg);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38\n"
     ]
    }
   ],
   "source": [
    "unique_turkers_pos = np.unique(pos['WorkerId'], return_counts=True)\n",
    "print(len(unique_turkers_pos[0]))\n",
    "turker_df = pd.DataFrame(zip(unique_turkers_pos[0], unique_turkers_pos[1]))\n",
    "sorted(turker_df)\n",
    "df = pd.DataFrame(turker_df.sort_values(by=1, ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f39a5c0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar',x=0,y=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33333333333333337"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "y1 = [0,1,2,3,4,0,1,2,3,4,0,1,2,3,4]\n",
    "y2 = [0,1,2,2,4,1,2,3,0,0,0,2,2,4,4]\n",
    "cohen_kappa_score(y1,y2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: \n",
    "                                                            (x=='Neutral').sum()).reset_index(name='Neutral')\n",
    "df2=pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: \n",
    "                                                            (x=='Positive').sum()).reset_index(name='Positive')\n",
    "df3=pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: \n",
    "                                                            (x=='Negative').sum()).reset_index(name='Negative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.concat([df1, df2, df3], p='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Turker        A    B    C\n",
      "SENTIMENT1  Neg  Neg  Pos\n",
      "SENTIMENT2  Neg  NaN  Pos\n",
      "SENTIMENT3  NaN  NaN  Neu\n",
      "REVIEW        5    3   13\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(2016)\n",
    "\n",
    "df = pd.DataFrame({'Turker': ['A', 'C', 'B', 'A', 'C', 'C'],\n",
    "                   'SENTIMENT': ['Neg', 'Pos', 'Neg', 'Neg', 'Pos', 'Neu'],\n",
    "                   'REVIEW': [1, 2, 3, 4, 5, 6]})\n",
    "\n",
    "grouped = df.groupby('Turker')\n",
    "values = grouped['REVIEW'].agg('sum')\n",
    "id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()\n",
    "id_df = id_df.rename(columns={i: 'SENTIMENT{}'.format(i + 1) for i in range(id_df.shape[1])})\n",
    "result = pd.concat([id_df, values], axis=1)\n",
    "print(result.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Turker</th>\n",
       "      <th>REVIEW</th>\n",
       "      <th>SENTIMENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Neg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>Pos</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>Neg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>Neg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>C</td>\n",
       "      <td>Pos</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>Neu</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Turker REVIEW  SENTIMENT\n",
       "0      A    Neg          1\n",
       "1      C    Pos          2\n",
       "2      B    Neg          3\n",
       "3      A    Neg          4\n",
       "4      C    Pos          5\n",
       "5      C    Neu          6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "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"
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 },
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