nlp-lab/Project/simple_approach/Evaluation_sentiment_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /home/jonas/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import simple_twitter_learning as stl\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rc('font', family='symbola', size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset already downloaded\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "if [ ! -e 'dataset_sentiment.csv' ]\n",
    "then\n",
    "    echo \"downloading dataset\"\n",
    "    wget https://raw.githubusercontent.com/SmartDataAnalytics/MA-INF-4222-NLP-Lab/master/2018_SoSe/exercises/dataset_sentiment.csv\n",
    "else\n",
    "    echo \"dataset already downloaded\"\n",
    "fi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* plot function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_score(s):\n",
    "    #(pos, neg, neu)^T\n",
    "    return s[0] - s[1]\n",
    "\n",
    "def plot_sentiment_space(predicted_sentiment_vectors_list, top_sentiments, top_emojis, style=['bo']):\n",
    "    # sentiment score axis\n",
    "    top_X = np.array([sentiment_score(x) for x in top_sentiments])\n",
    "    \n",
    "\n",
    "    # neutral axis:\n",
    "    top_Y = np.array([x[2] for x in top_sentiments])\n",
    "    \n",
    "\n",
    "    fig_1, ax_1 = plt.subplots(figsize=(7.5,5))\n",
    "    plt.title(\"sentiment-score-plot\")\n",
    "    plt.xlabel(\"sentiment score\")\n",
    "    plt.ylabel(\"neutrality\")\n",
    "    plt.xlim([np.min(top_X),np.max(top_X)])\n",
    "    plt.ylim([np.min(top_Y),np.max(top_Y)])\n",
    "    for i in range(len(top_X)):\n",
    "        plt.text(top_X[i], top_Y[i], top_emojis[i])\n",
    "    for i in range(len(predicted_sentiment_vectors_list)):\n",
    "        pred_X = np.array([sentiment_score(x) for x in predicted_sentiment_vectors_list[i]])\n",
    "        pred_Y = np.array([x[2] for x in predicted_sentiment_vectors_list[i]])\n",
    "        plt.plot(pred_X, pred_Y, style[i], alpha=0.5)\n",
    "    #plt.savefig(\"val-error_sentiment-plot\" + str(datetime.datetime.now()) +  \".png\", bbox_inches='tight')\n",
    "\n",
    "    # sentiment score axis\n",
    "    top_X = np.array([x[0] for x in top_sentiments])\n",
    "    \n",
    "\n",
    "    # neutral axis:\n",
    "    top_Y = np.array([x[1] for x in top_sentiments])\n",
    "    \n",
    "\n",
    "    fig_2, ax_2 = plt.subplots(figsize=(7.5,5))\n",
    "    plt.title(\"positive-negative-plot\")\n",
    "    plt.xlabel(\"positive\")\n",
    "    plt.ylabel(\"negative\")\n",
    "    plt.xlim([np.min(top_X),np.max(top_X)])\n",
    "    plt.ylim([np.min(top_Y),np.max(top_Y)])\n",
    "    for i in range(len(top_X)):\n",
    "        plt.text(top_X[i], top_Y[i], top_emojis[i])\n",
    "    for i in range(len(predicted_sentiment_vectors_list)):\n",
    "        pred_X = np.array([x[0] for x in predicted_sentiment_vectors_list[i]])\n",
    "        pred_Y = np.array([x[1] for x in predicted_sentiment_vectors_list[i]])\n",
    "        plt.plot(pred_X, pred_Y, style[i], alpha=0.5)\n",
    "    #'plt.savefig(\"val-error_positive-negative-plot\" + str(datetime.datetime.now()) + \".png\", bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* read data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset_sentiment.csv')\n",
    "df = df[['text','sentiment']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>text</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nancyleegrahn how did everyone feel about th...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>scottwalker didnt catch the full gopdebate l...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tjmshow no mention of tamir rice and the gop...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>robgeorge that carly fiorina is trending  ho...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>danscavino gopdebate w realdonaldtrump deliv...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text sentiment\n",
       "0    nancyleegrahn how did everyone feel about th...   Neutral\n",
       "1    scottwalker didnt catch the full gopdebate l...  Positive\n",
       "2    tjmshow no mention of tamir rice and the gop...   Neutral\n",
       "3    robgeorge that carly fiorina is trending  ho...  Positive\n",
       "4    danscavino gopdebate w realdonaldtrump deliv...  Positive"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'] = df['text'].apply(lambda x: x.lower())\n",
    "df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))\n",
    "df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-Z0-9\\s]','',x)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg = np.array([df['sentiment'][i] == 'Negative' for i in range(df.shape[0])])\n",
    "pos = np.array([df['sentiment'][i] == 'Positive' for i in range(df.shape[0])])\n",
    "neu = np.array([df['sentiment'][i] == 'Neutral' for i in range(df.shape[0])])\n",
    "\n",
    "text = np.array(df['text'].tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* load pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_file = \"/home/jonas/Dokumente/NLP_DATA/python_dumps/pipelines/tfidf_final/final_epoch01.pipeline\"\n",
    "pm = stl.pipeline_manager.load_from_pipeline_file(pipeline_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* plot statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_20 = list(\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\")\n",
    "top_20_sents = stl.emoji2sent(top_20)\n",
    "\n",
    "pred_pos = pm.predict(text[pos])\n",
    "pred_neg = pm.predict(text[neg])\n",
    "pred_neu = pm.predict(text[neu])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#stl.plot_sentiment_space(predicted_sentiment_vectors=pred_pos, top_sentiments=top_20_sents, top_emojis=top_20, style='go')\n",
    "#stl.plot_sentiment_space(predicted_sentiment_vectors=pred_neg, top_sentiments=top_20_sents, top_emojis=top_20, style='ro')\n",
    "#stl.plot_sentiment_space(predicted_sentiment_vectors=pred_neu, top_sentiments=top_20_sents, top_emojis=top_20, style='bo')\n",
    "plot_sentiment_space(predicted_sentiment_vectors_list=[pred_pos,pred_neg,pred_neu], top_sentiments=top_20_sents, top_emojis=top_20, style=['g+','b+','r+'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## user labeled sentiment dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../sentiment_sentences.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg = np.array([df['sent'][i] == 'n' for i in range(df.shape[0])])\n",
    "pos = np.array([df['sent'][i] == 'p' for i in range(df.shape[0])])\n",
    "\n",
    "text = np.array(df['text'].tolist())\n",
    "\n",
    "pred_pos = pm.predict(text[pos])\n",
    "pred_neg = pm.predict(text[neg])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sentiment_space(predicted_sentiment_vectors_list=[pred_pos,pred_neg], top_sentiments=top_20_sents, top_emojis=top_20, style=['go','ro'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>text</th>\n",
       "      <th>sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I am so happy</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>i love my life</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i really like this sunshine</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>while doing sport i feel free</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i is terrible to learn when the weather is thi...</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>i am almost crying when i run out of ice cream</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fuck off</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>you are my best friend ever</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>i love my mommy</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>i prefer oral exams</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>best cocktail is the mojito especial</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>you got a beautiful wedding</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>you husband is this handsome</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>this is such a sneaky bitch</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>you're kidding</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>you are really too drunk</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>you start being annoying</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>i love my mac</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>but it would be better if they are less expensive</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>you are a bad friend</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>I am in love with my new laptop</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>You piss me off</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>I hate school</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>I'm so mad at you</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Seeing you cry, makes me sad</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>You are so lovely</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Fish is disgusting</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>At times I feel so depressed</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Summer makes me happy</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>I want to dance and sing aloud</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Everything will be ok</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>why are you upset?</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>I dont like it when it rains</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>I dont care at all</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>you are such a bitch</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>I dont understand what this is all about</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>give me a break, idiot!</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>please dont leave me</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>I love cuddling with my dog</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>I want to die</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>that is really sad!</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>why are you saying that, you moron!</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>OMG! i love that cat soo much!</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>can you feel the love tonight</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>i get so emotional when i watch lion king</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>i really hate you</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>that is disgusting</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>my heart is broken</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>i love these sunny days</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>this gave me positive feelings</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>i am sick</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>i am tired</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>are you sick or tired?</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>you are so cute and sweet</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>i just lost my mind</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>i hate all people</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>you are just dumb</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>i will get lucky</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>i like ice cream</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>go home you idiot</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 text sent\n",
       "0                                       I am so happy    p\n",
       "1                                      i love my life    p\n",
       "2                         i really like this sunshine    p\n",
       "3                       while doing sport i feel free    p\n",
       "4   i is terrible to learn when the weather is thi...    n\n",
       "5      i am almost crying when i run out of ice cream    n\n",
       "6                                            fuck off    n\n",
       "7                         you are my best friend ever    p\n",
       "8                                     i love my mommy    p\n",
       "9                                 i prefer oral exams    p\n",
       "10               best cocktail is the mojito especial    p\n",
       "11                        you got a beautiful wedding    p\n",
       "12                       you husband is this handsome    p\n",
       "13                        this is such a sneaky bitch    n\n",
       "14                                     you're kidding    n\n",
       "15                           you are really too drunk    n\n",
       "16                           you start being annoying    n\n",
       "17                                      i love my mac    p\n",
       "18  but it would be better if they are less expensive    n\n",
       "19                               you are a bad friend    n\n",
       "20                    I am in love with my new laptop    p\n",
       "21                                    You piss me off    n\n",
       "22                                      I hate school    n\n",
       "23                                  I'm so mad at you    n\n",
       "24                       Seeing you cry, makes me sad    n\n",
       "25                                  You are so lovely    p\n",
       "26                                 Fish is disgusting    n\n",
       "27                       At times I feel so depressed    n\n",
       "28                              Summer makes me happy    p\n",
       "29                     I want to dance and sing aloud    p\n",
       "30                              Everything will be ok    p\n",
       "31                                 why are you upset?    n\n",
       "32                       I dont like it when it rains    n\n",
       "33                                 I dont care at all    n\n",
       "34                               you are such a bitch    n\n",
       "35           I dont understand what this is all about    n\n",
       "36                            give me a break, idiot!    n\n",
       "37                               please dont leave me    n\n",
       "38                        I love cuddling with my dog    p\n",
       "39                                      I want to die    n\n",
       "40                                that is really sad!    n\n",
       "41                why are you saying that, you moron!    n\n",
       "42                     OMG! i love that cat soo much!    p\n",
       "43                      can you feel the love tonight    p\n",
       "44          i get so emotional when i watch lion king    p\n",
       "45                                  i really hate you    n\n",
       "46                                 that is disgusting    n\n",
       "47                                 my heart is broken    n\n",
       "48                            i love these sunny days    p\n",
       "49                     this gave me positive feelings    p\n",
       "50                                          i am sick    n\n",
       "51                                         i am tired    n\n",
       "52                             are you sick or tired?    n\n",
       "53                          you are so cute and sweet    p\n",
       "54                                i just lost my mind    n\n",
       "55                                  i hate all people    n\n",
       "56                                  you are just dumb    n\n",
       "57                                   i will get lucky    p\n",
       "58                                   i like ice cream    p\n",
       "59                                  go home you idiot    n"
      ]
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