Notebooks/II. Polarity and Emotion Recognition/5. Rule Based Fine Grained Lexicon Only Approach.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import ast\n",
    "import pickle\n",
    "from EmotionAnalysis.DataSchemaExtractionParsing import *\n",
    "from EmotionAnalysis.DataPreProcessing import *\n",
    "from EmotionAnalysis.SentSemanticModule import *\n",
    "from EmotionAnalysis.SentTweetModule import *\n",
    "from EmotionAnalysis.SentSyntacticModule import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Rule-Based Lexicon:\n",
    "#### Strategy:\n",
    "* Loading Nava Representation of Tweets\n",
    "* Loading NRC Lexicon corresponding to the language of the dataset\n",
    "* Computing Word Level Vectors of Emotional and Polarity Scores \n",
    "* Computing Sentence Level Vectors of Emotional and Polarity Scores\n",
    "* Selecting Dominant Emotion and Polarity for each emotion\n",
    "* Storing both dominant emotion and polarities and their corresponding score vectors for results aggregation purpose (since we want to aggregate by canton which has a set of tweets)\n",
    "* Visualizing distribution of results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Loading NAVA Representation of Tweets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "###### STEP 1: Loading Data with nava affective representation:\n",
    "# HERE YOU CAN CHANGE THE NAME OF THE FILE FROM WHICH TO LOAD THE DATA\n",
    "\n",
    "tweets_df = pd.read_csv('../../Results/Sample Affective Representation.csv')\n",
    "\n",
    "nava_repr = tweets_df['Nava Representation'] \n",
    "\n",
    "# Convert nava_tweets \n",
    "nava_tweets = []\n",
    "for i in range(0, len(nava_repr)):\n",
    "    result = ast.literal_eval(nava_repr[i])\n",
    "    nava_tweets.append(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Loading NRC Lexicon dictionary corresponding to the language of the dataset:\n",
    "\n",
    "###### NB: \n",
    "Please have a look at notebook PrepareLexicon for more details on how this lexicon was prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6468"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../../NRCLexicon/English/english_lexicon.pickle', 'rb') as handle:\n",
    "    lexicon_dict = pickle.load(handle)\n",
    "len(lexicon_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Computing Word Level Vectors of Emotional and Polarity Scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Both Emotion Recognition and Sentiment Analysis:\n",
    "# Each word has a vector of 10 elements where the indices are for the following emotions/sentiments:\n",
    "# 0: Anger\n",
    "# 1: Anticipation\n",
    "# 2: Disgust\n",
    "# 3: Fear\n",
    "# 4: Joy\n",
    "# 5: Negative\n",
    "# 6: Positive\n",
    "# 7: Sadness\n",
    "# 8: Surprise\n",
    "# 9: Trust\n",
    "matrix_sentences_lexicon = compute_matrix_sentences_list_lexicon(nava_tweets,lexicon_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 1, 0, 0, 1, 0, 1, 0, 1, 1],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [1, 0, 0, 1, 0, 1, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_sentences_lexicon[10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Computing Tweet Level Vectors of Emotional and Polarity Scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.125, 0.25, 0.0, 0.125, 0.125, 0.0, 0.125, 0.125]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Emotion Recognition\n",
    "sentence_vectors_lexicon = compute_sentence_emotion_vectors(matrix_sentences_lexicon)\n",
    "sentence_vectors_lexicon[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.125, 0.125]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sentiment Analysis\n",
    "sentence_vectors_sent = compute_sentence_sentiment_vectors(matrix_sentences_lexicon)\n",
    "sentence_vectors_sent[10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Selecting Dominant Emotion and Polarity for each tweet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "emotionalities = compute_emotionalities(sentence_vectors_lexicon)\n",
    "\n",
    "emo_dict = {\n",
    "    0: 'Anger',\n",
    "    1: 'Anticipation',\n",
    "    2: 'Disgust',\n",
    "    3: 'Fear',\n",
    "    4: 'Joy',\n",
    "    5: 'Sadness',\n",
    "    6: 'Surprise',\n",
    "    7: 'Trust',\n",
    "    8: 'Neutral'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sentiments = compute_sentiments(sentence_vectors_sent,emotionalities)\n",
    "\n",
    "sent_dict = {\n",
    "    0: \"Negative\",\n",
    "    1: \"Positive\",\n",
    "    2: \"Neutral\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "emotions = []\n",
    "senti = []\n",
    "for i in range(0,len(emotionalities)):\n",
    "    emotions.append(emo_dict[emotionalities[i]])\n",
    "    senti.append(sent_dict[sentiments[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Storing both dominant emotion and polarities and their corresponding score vectors for results aggregation purpose (since we want to aggregate by canton which has a set of tweets):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tweets_df['Emotion'] = emotions\n",
    "\n",
    "tweets_df['Emotion Vectors'] = sentence_vectors_lexicon\n",
    "\n",
    "tweets_df['Sentiment'] = senti\n",
    "\n",
    "tweets_df['Sentiment Vectors'] = sentence_vectors_sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tweets_df.to_csv('../../Results/RuleBasedLexiconResultsSampleEnglishData.csv',encoding =\"utf-8\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Visualizing distribution of results:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.1. Visualizing Emotion Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2bf90abd10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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EjGYiPfpzgENGtC0ErrG9J3AtsAhA0l7AkcB84FDgTDVH3QDOAk6wPQ+YJ2nkz4yIiC4Y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\nIir3P7dIPEPOkWs4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2bf90abc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from collections import Counter\n",
    "frequency = Counter(tweets_df['Emotion'])\n",
    "df = pd.DataFrame.from_dict(frequency, orient='index')\n",
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L4INQx6MdPp2guiFD5F67w3n0BfMv0/M59XBm+1NEi+1PBZ9m0W/ErE6Po7R4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\nk6AMwzCMsPT/ARy7sj1vkynKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2bf9004a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "a=np.random.random(9)\n",
    "cs=cm.Set1(np.arange(9)/9.)\n",
    "f=plt.figure()\n",
    "ax=f.add_subplot(111, aspect='equal')\n",
    "patches, texts = plt.pie(df, colors=cs, startangle=90)\n",
    "labels = df.index\n",
    "p=plt.pie(df, colors=cs, labels = labels)\n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "plt.title(\"Rule Based Fine-grained Emotion Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.2. Visualizing Sentiment Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2bf901cb90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2bf8fc9590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from collections import Counter\n",
    "frequency = Counter(tweets_df['Sentiment'])\n",
    "df = pd.DataFrame.from_dict(frequency, orient='index')\n",
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZytJK6t61p+oowtatuDuCloUfZPoNYRvj9qBQ01wAbladJR1JgVKAiHqCtGOO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\nH8Ma5MaRAVnmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2bf6dc8c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "a=np.random.random(3)\n",
    "cs=cm.Set1(np.arange(3)/3.)\n",
    "f=plt.figure()\n",
    "ax=f.add_subplot(111, aspect='equal')\n",
    "patches, texts = plt.pie(df, colors=cs, startangle=90)\n",
    "labels = df.index\n",
    "p=plt.pie(df, colors=cs, labels = labels)\n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "plt.title(\"Rule Based Sentiment Emotion Distribution\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}