03-Airline-Sentiment-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 03-Airline-Sentiment-Analysis.ipynb\n",
    "* **Script**: 03-Airline-Sentiment-Analysis.ipynb\n",
    "* **Purpose**: Compare performance of rule-based (e.g. TextBlob, VADER) and supervised ML-based (e.g. SVM, Naive Bayes) sentiment classifiers.\n",
    "* **Input**: NLTK Twitter Samples, data/crowdflower/airline_sentiment_with_tb_vader.pkl\n",
    "* **Output**: graphics/twees_by_sentiment_airline.png, graphics/sentiment_analyzer_accuracy.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import pylab\n",
    "%matplotlib inline\n",
    "\n",
    "from nltk.corpus import twitter_samples\n",
    "from nltk.tokenize.casual import TweetTokenizer\n",
    "\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, ENGLISH_STOP_WORDS\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define Preprocessing and Tokenizer Functions to Read into Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "    text = re.sub(r\"(?:\\https?\\://)\\S+\", \"\", text) #remove urls\n",
    "    text = re.sub('\\@(\\w+)', \" \", text).replace(\": \",\"\") #remove usernames\n",
    "    text = re.sub('#(\\w+)', \" \", text) #remove hashtags\n",
    "    text = text.replace(\"RT \",\"\") #remove RT Symbols\n",
    "    text = text.replace(\"RT: \",\"\") #remove RT Symbols\n",
    "    text = re.sub(\"[^a-zA-Z,]+\", \" \", text) #remove other non-alpha characters\n",
    "    text = text.strip(\" \") #remove leading and trailing whitespace\n",
    "    \n",
    "    return text\n",
    "\n",
    "def tokenize(text):\n",
    "    tokenizer = TweetTokenizer(preserve_case=False, reduce_len=True, strip_handles=True)  \n",
    "    \n",
    "    return [x for x in tokenizer.tokenize(preprocess(text)) if len(x)>=4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read-In NLTK Twitter Sentiment Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    5000\n",
      "0    5000\n",
      "Name: sentiment, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>hopeless for tmr :(</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Everything in the kids section of IKEA is so c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>@Hegelbon That heart sliding into the waste ba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>“@ketchBurning: I hate Japanese call him \"bani...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Dang starting next week I have \"work\" :(</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment                                               text\n",
       "0          1                                hopeless for tmr :(\n",
       "1          1  Everything in the kids section of IKEA is so c...\n",
       "2          1  @Hegelbon That heart sliding into the waste ba...\n",
       "3          1  “@ketchBurning: I hate Japanese call him \"bani...\n",
       "4          1           Dang starting next week I have \"work\" :("
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def read_demo_tweets():\n",
    "    tweets = []\n",
    "    for tweet in twitter_samples.strings(\"negative_tweets.json\"):\n",
    "        tweets.append({\"text\":tweet, \"sentiment\":1})\n",
    "        \n",
    "    for tweet in twitter_samples.strings(\"positive_tweets.json\"):\n",
    "        tweets.append({\"text\":tweet, \"sentiment\":0})\n",
    "        \n",
    "    return pd.DataFrame(tweets)\n",
    "\n",
    "\n",
    "demo_tweets=read_demo_tweets()\n",
    "print demo_tweets.sentiment.value_counts(dropna=False)\n",
    "demo_tweets.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in CrowdFlower Data. Split Unambigous Tweets into Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_unit_id</th>\n",
       "      <th>_trusted_judgments</th>\n",
       "      <th>airline_sentiment:confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text_blob_sentiment</th>\n",
       "      <th>vader_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>681448150</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>681448153</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>681448156</td>\n",
       "      <td>3</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.390625</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>681448158</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>-0.5984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>681448159</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.350000</td>\n",
       "      <td>-0.5829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    _unit_id  _trusted_judgments  airline_sentiment:confidence  \\\n",
       "0  681448150                   3                        1.0000   \n",
       "1  681448153                   3                        0.3486   \n",
       "2  681448156                   3                        0.6837   \n",
       "3  681448158                   3                        1.0000   \n",
       "4  681448159                   3                        1.0000   \n",
       "\n",
       "          airline                                               text  \\\n",
       "0  Virgin America                @VirginAmerica What @dhepburn said.   \n",
       "1  Virgin America  @VirginAmerica plus you've added commercials t...   \n",
       "2  Virgin America  @VirginAmerica I didn't today... Must mean I n...   \n",
       "3  Virgin America  @VirginAmerica it's really aggressive to blast...   \n",
       "4  Virgin America  @VirginAmerica and it's a really big bad thing...   \n",
       "\n",
       "   sentiment  text_blob_sentiment  vader_sentiment  \n",
       "0          0             0.000000           0.0000  \n",
       "1          0             0.000000           0.0000  \n",
       "2          0            -0.390625           0.0000  \n",
       "3          1             0.006250          -0.5984  \n",
       "4          1            -0.350000          -0.5829  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf = pd.read_pickle(\"data/crowdflower/airline_sentiment_with_tb_vader.pkl\")\n",
    "cf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    14640.000000\n",
      "mean         0.048714\n",
      "std          0.325888\n",
      "min         -1.000000\n",
      "25%         -0.010000\n",
      "50%          0.000000\n",
      "75%          0.200000\n",
      "max          1.000000\n",
      "Name: text_blob_sentiment, dtype: float64\n",
      "count    14640.000000\n",
      "mean         0.055417\n",
      "std          0.456556\n",
      "min         -0.966800\n",
      "25%         -0.296000\n",
      "50%          0.000000\n",
      "75%          0.440400\n",
      "max          0.976000\n",
      "Name: vader_sentiment, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print cf.text_blob_sentiment.describe()\n",
    "print cf.vader_sentiment.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4194, 8) (10427, 8) (7298, 8) (3129, 8)\n"
     ]
    }
   ],
   "source": [
    "cf_unambig = cf.ix[(cf[\"_trusted_judgments\"]>2) & (cf[\"airline_sentiment:confidence\"]==1.00)]\n",
    "cf_ambig = cf.ix[(cf[\"_trusted_judgments\"]>2) & (cf[\"airline_sentiment:confidence\"]<1.00)]\n",
    "\n",
    "cf_train, cf_test = train_test_split(cf_unambig, test_size=0.3, random_state=4444)\n",
    "\n",
    "print cf_ambig.shape, cf_unambig.shape, cf_train.shape, cf_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graph Positive/ Neutral vs. Negative Ratings by Airline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Virgin America', 'Delta', 'Southwest', 'American', 'US Airways', 'United']\n"
     ]
    },
    {
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f/zzXXXdTLr30qhxwwMFJllx9dvnlv0+lUsknP3lorrlmUq677sZM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\nKUpBJBERERERERERKer/AQwkh/SPfeImAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dde0ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "negative = cf_unambig.ix[cf_unambig.sentiment==1].groupby(by=\"airline\", as_index=False)[\"_unit_id\"].count()\n",
    "positive = cf_unambig.ix[cf_unambig.sentiment==0].groupby(by=\"airline\", as_index=False)[\"_unit_id\"].count()\n",
    "\n",
    "negative.rename(columns={\"_unit_id\":\"negative_tweets\"}, inplace=True)\n",
    "positive.rename(columns={\"_unit_id\":\"positive_tweets\"}, inplace=True)\n",
    "total = pd.merge(negative, positive, on=\"airline\")\n",
    "total[\"total_tweets\"] = total.negative_tweets + total.positive_tweets\n",
    "total.sort_values(by=\"total_tweets\", ascending=True, inplace=True)\n",
    "print total.airline.tolist()\n",
    "\n",
    "blue = sns.color_palette(\"deep\")[0]\n",
    "red = sns.color_palette(\"deep\")[2]\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "p1=plt.barh(np.arange(6), total.negative_tweets, 0.8, color=red, label=\"Negative\")\n",
    "p2=plt.barh(np.arange(6), total.positive_tweets, 0.8, color=blue, left=total.negative_tweets,label=\"Positive/ Neutral\")\n",
    "\n",
    "#plt.title(\"Tweets by Sentiment and Airline\", fontsize=32)\n",
    "plt.xlabel(\"Number of Tweets (Feb 2015)\", fontsize=26)\n",
    "#plt.yticks([(x+(0.75/2)) for x in range(0,6)],[])\n",
    "plt.tick_params(axis=\"x\", labelsize=24)\n",
    "plt.tick_params(axis=\"y\", colors=\"white\")\n",
    "plt.legend(loc=\"best\", fontsize=26)\n",
    "plt.savefig(\"graphics/twees_by_sentiment_airline.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid Search to Optimize Lexicon-Based Sentiment Analyzers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text Blob 0.5: (0.5, 0.73761585170981148)\n",
      "Text Blob 0.1: (0.29999999999999982, 0.73761585170981148)\n",
      "Text Blob 0.05: (0.40000000000000124, 0.74177053371684243)\n",
      "\n",
      "VADER 0.5: (0.5, 0.75775007989773091)\n",
      "VADER 0.1: (0.39999999999999969, 0.76478108021732183)\n",
      "VADER 0.05: (0.3500000000000012, 0.76861617130073501)\n"
     ]
    }
   ],
   "source": [
    "def lexicon_grid_search(variable, test, step):\n",
    "    max_accuracy = 0\n",
    "    max_param = np.nan\n",
    "    \n",
    "    for x in np.arange(-1, 1, step):\n",
    "        pred_y = []\n",
    "        \n",
    "        for each in test[variable]:\n",
    "            if each < x:\n",
    "                pred_y.append(1) #negative\n",
    "            else:\n",
    "                pred_y.append(0) #positive/ neutral\n",
    "            \n",
    "        true_y = test[\"sentiment\"]\n",
    "        accuracy = accuracy_score(true_y, pred_y)\n",
    "            \n",
    "        if accuracy > max_accuracy:\n",
    "            max_accuracy = accuracy\n",
    "            max_param = x\n",
    "            \n",
    "    return (max_param, max_accuracy)\n",
    "\n",
    "print \"Text Blob 0.5: \"+str(lexicon_grid_search(\"text_blob_sentiment\", cf_test, 0.5))\n",
    "print \"Text Blob 0.1: \"+str(lexicon_grid_search(\"text_blob_sentiment\", cf_test, 0.1))\n",
    "print \"Text Blob 0.05: \"+str(lexicon_grid_search(\"text_blob_sentiment\", cf_test, 0.05))\n",
    "print \"\"\n",
    "print \"VADER 0.5: \"+str(lexicon_grid_search(\"vader_sentiment\", cf_test, 0.5))\n",
    "print \"VADER 0.1: \"+str(lexicon_grid_search(\"vader_sentiment\", cf_test, 0.1))\n",
    "print \"VADER 0.05: \"+str(lexicon_grid_search(\"vader_sentiment\", cf_test, 0.05))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimal TextBlob Sentiment Classifer attains a 77% accuracy on the test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Naive Bayes and SVM Models Trained on NLTK and Crowdflower Corpuses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "air_travel_sws = ENGLISH_STOP_WORDS.union([\"aa\",\"united\",\"usairways\",\"americanair\",\"southwestair\",\"jetblue\",\"http\",\\\n",
    "                                           \"virginamerica\",\"amp\",\"flight\",\"flights\",\"plane\",\"gate\",\"flightled\",\\\n",
    "                                           \"bag\",\"airline\",\"airport\",\"fly\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_count_model(classifier, train, test, sws, binary):\n",
    "    vectorizer = CountVectorizer(decode_error=\"ignore\", ngram_range=(1,1), min_df=2, max_df=0.2, stop_words=sws,\\\n",
    "                                 tokenizer=tokenize, binary=binary)\n",
    "    \n",
    "    vectorizer.fit(train[\"text\"])\n",
    "    \n",
    "    train_X = vectorizer.transform(train[\"text\"])\n",
    "    test_X = vectorizer.transform(test[\"text\"])\n",
    "    \n",
    "    train_y = train[\"sentiment\"]\n",
    "    test_y = test[\"sentiment\"]\n",
    "    \n",
    "    clf = classifier.fit(train_X, train_y)\n",
    "    \n",
    "    #print \"Confusion Matrix: \"\n",
    "    #print confusion_matrix(test_y, clf.predict(test_X))\n",
    "    \n",
    "    accuracy = accuracy_score(test_y, clf.predict(test_X))\n",
    "    features = zip(vectorizer.get_feature_names(), clf.coef_[0])\n",
    "    top10_features = sorted(features, reverse=True, key=lambda x: x[1])[:10]\n",
    "    \n",
    "    print \"Accuracy: \"+str(accuracy)\n",
    "    \n",
    "    return [accuracy, [x[0] for x in top10_features]]\n",
    "    \n",
    "\n",
    "def fit_tfidf_model(classifier, train, test, sws, binary):\n",
    "    vectorizer = TfidfVectorizer(decode_error=\"ignore\", ngram_range=(1,1), min_df=2, max_df=0.2, stop_words=sws,\n",
    "                                 tokenizer=tokenize, binary=binary)\n",
    "        \n",
    "    vectorizer.fit(train[\"text\"])\n",
    "    \n",
    "    train_X = vectorizer.transform(train[\"text\"])\n",
    "    test_X = vectorizer.transform(test[\"text\"])\n",
    "    \n",
    "    train_y = train[\"sentiment\"]\n",
    "    test_y = test[\"sentiment\"]\n",
    "    \n",
    "    clf = classifier.fit(train_X, train_y)\n",
    "    \n",
    "    #print \"Confusion Matrix: \"\n",
    "    #print confusion_matrix(test_y, clf.predict(test_X))\n",
    "    \n",
    "    accuracy = accuracy_score(test_y, clf.predict(test_X))\n",
    "    features = zip(vectorizer.get_feature_names(), clf.coef_[0])\n",
    "    top10_features=sorted(features, reverse=True, key=lambda x: x[1])[:10]\n",
    "    \n",
    "    print \"Accuracy: \"+str(accuracy)\n",
    "    \n",
    "    return [accuracy, [x[0] for x in top10_features]]\n",
    "\n",
    "\n",
    "def test_models(function, train, sws, label):\n",
    "    m1=function(BernoulliNB(binarize=None), train, cf_test, sws, True)\n",
    "    m2=function(MultinomialNB(), train, cf_test, sws, False)\n",
    "    m3=function(LinearSVC(), train, cf_test, sws, False)\n",
    "    \n",
    "    data = [{\"clf\":\"Bernoulli NB\", \"accuracy\":m1[0]*100, \"features\":m1[1], \"label\":label},\\\n",
    "            {\"clf\":\"Multinomial NB\", \"accuracy\":m2[0]*100, \"features\":m2[1], \"label\":label},\\\n",
    "            {\"clf\":\"Linear SVM\",\"accuracy\":m3[0]*100, \"features\":m3[1], \"label\":label}]\n",
    "    \n",
    "    return pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.725151805689\n",
      "Accuracy: 0.743368488335\n",
      "Accuracy: 0.651006711409\n",
      "\n",
      "Accuracy: 0.715244487057\n",
      "Accuracy: 0.732821987856\n",
      "Accuracy: 0.666027484819\n",
      "\n",
      "Accuracy: 0.856184084372\n",
      "Accuracy: 0.837647810802\n",
      "Accuracy: 0.841482901886\n",
      "\n",
      "Accuracy: 0.814956855225\n",
      "Accuracy: 0.822946628316\n",
      "Accuracy: 0.851390220518\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>clf</th>\n",
       "      <th>features</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72.515181</td>\n",
       "      <td>Bernoulli NB</td>\n",
       "      <td>[miss, just, want, like, sorry, time, love, kn...</td>\n",
       "      <td>General Tweets \\n(Count Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74.336849</td>\n",
       "      <td>Multinomial NB</td>\n",
       "      <td>[miss, just, want, like, sorry, follow, time, ...</td>\n",
       "      <td>General Tweets \\n(Count Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.100671</td>\n",
       "      <td>Linear SVM</td>\n",
       "      <td>[letting, emotional, anybody, poorly, medicine...</td>\n",
       "      <td>General Tweets \\n(Count Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.524449</td>\n",
       "      <td>Bernoulli NB</td>\n",
       "      <td>[miss, want, sorry, just, like, love, know, fo...</td>\n",
       "      <td>General Tweets \\n(TF-IDF Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73.282199</td>\n",
       "      <td>Multinomial NB</td>\n",
       "      <td>[miss, want, sorry, just, like, follow, love, ...</td>\n",
       "      <td>General Tweets \\n(TF-IDF Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.602748</td>\n",
       "      <td>Linear SVM</td>\n",
       "      <td>[shame, anybody, emotional, missing, justin, l...</td>\n",
       "      <td>General Tweets \\n(TF-IDF Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85.618408</td>\n",
       "      <td>Bernoulli NB</td>\n",
       "      <td>[cancelled, service, hours, customer, hold, ti...</td>\n",
       "      <td>Airline Tweets \\n(Count Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83.764781</td>\n",
       "      <td>Multinomial NB</td>\n",
       "      <td>[cancelled, service, hours, customer, hold, ti...</td>\n",
       "      <td>Airline Tweets \\n(Count Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84.148290</td>\n",
       "      <td>Linear SVM</td>\n",
       "      <td>[denied, screwed, miserable, worse, dulles, br...</td>\n",
       "      <td>Airline Tweets \\n(Count Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.495686</td>\n",
       "      <td>Bernoulli NB</td>\n",
       "      <td>[cancelled, hours, service, hold, customer, de...</td>\n",
       "      <td>Airline Tweets \\n(TF-IDF Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82.294663</td>\n",
       "      <td>Multinomial NB</td>\n",
       "      <td>[cancelled, hours, service, hold, customer, de...</td>\n",
       "      <td>Airline Tweets \\n(TF-IDF Vectorized)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85.139022</td>\n",
       "      <td>Linear SVM</td>\n",
       "      <td>[worst, hours, customers, disappointed, hour, ...</td>\n",
       "      <td>Airline Tweets \\n(TF-IDF Vectorized)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    accuracy             clf  \\\n",
       "0  72.515181    Bernoulli NB   \n",
       "1  74.336849  Multinomial NB   \n",
       "2  65.100671      Linear SVM   \n",
       "0  71.524449    Bernoulli NB   \n",
       "1  73.282199  Multinomial NB   \n",
       "2  66.602748      Linear SVM   \n",
       "0  85.618408    Bernoulli NB   \n",
       "1  83.764781  Multinomial NB   \n",
       "2  84.148290      Linear SVM   \n",
       "0  81.495686    Bernoulli NB   \n",
       "1  82.294663  Multinomial NB   \n",
       "2  85.139022      Linear SVM   \n",
       "\n",
       "                                            features  \\\n",
       "0  [miss, just, want, like, sorry, time, love, kn...   \n",
       "1  [miss, just, want, like, sorry, follow, time, ...   \n",
       "2  [letting, emotional, anybody, poorly, medicine...   \n",
       "0  [miss, want, sorry, just, like, love, know, fo...   \n",
       "1  [miss, want, sorry, just, like, follow, love, ...   \n",
       "2  [shame, anybody, emotional, missing, justin, l...   \n",
       "0  [cancelled, service, hours, customer, hold, ti...   \n",
       "1  [cancelled, service, hours, customer, hold, ti...   \n",
       "2  [denied, screwed, miserable, worse, dulles, br...   \n",
       "0  [cancelled, hours, service, hold, customer, de...   \n",
       "1  [cancelled, hours, service, hold, customer, de...   \n",
       "2  [worst, hours, customers, disappointed, hour, ...   \n",
       "\n",
       "                                  label  \n",
       "0   General Tweets \\n(Count Vectorized)  \n",
       "1   General Tweets \\n(Count Vectorized)  \n",
       "2   General Tweets \\n(Count Vectorized)  \n",
       "0  General Tweets \\n(TF-IDF Vectorized)  \n",
       "1  General Tweets \\n(TF-IDF Vectorized)  \n",
       "2  General Tweets \\n(TF-IDF Vectorized)  \n",
       "0   Airline Tweets \\n(Count Vectorized)  \n",
       "1   Airline Tweets \\n(Count Vectorized)  \n",
       "2   Airline Tweets \\n(Count Vectorized)  \n",
       "0  Airline Tweets \\n(TF-IDF Vectorized)  \n",
       "1  Airline Tweets \\n(TF-IDF Vectorized)  \n",
       "2  Airline Tweets \\n(TF-IDF Vectorized)  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "general_count = test_models(fit_count_model, demo_tweets, \"english\", \"General Tweets \\n(Count Vectorized)\")\n",
    "print \"\"\n",
    "general_tfidf = test_models(fit_tfidf_model, demo_tweets, \"english\", \"General Tweets \\n(TF-IDF Vectorized)\")\n",
    "print \"\"\n",
    "airline_count = test_models(fit_count_model, cf_train, air_travel_sws, \"Airline Tweets \\n(Count Vectorized)\")\n",
    "print \"\"\n",
    "airline_tfidf = test_models(fit_tfidf_model, cf_train, air_travel_sws, \"Airline Tweets \\n(TF-IDF Vectorized)\")\n",
    "print \"\"\n",
    "\n",
    "results = pd.concat([general_count, general_tfidf, airline_count, airline_tfidf], axis=0)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Accuracy Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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h1mu7+/JZrm9fxD12/bw7J1YVAAAA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1NqDMY830EvYpCbdX9wTMmL7pLc9yX6Id9MkOdDI5PdMM1pzkThAq9ZZSqNcGxNVbyfLclnfzQa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\nYoMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e090950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "reds = sns.color_palette(\"Greens\")[2:5]\n",
    "blues = sns.color_palette(\"Blues\")[2:5]\n",
    "\n",
    "l1=plt.axhline(y=float(cf_test.sentiment.value_counts()[1]*100)/ cf_test.shape[0], color=\"black\")\n",
    "plt.axhline(y=lexicon_grid_search(\"text_blob_sentiment\", cf_test, 0.05)[1]*100, color=\"#ff00bf\")\n",
    "plt.axhline(y=lexicon_grid_search(\"vader_sentiment\", cf_test, 0.05)[1]*100, color=\"#8000ff\")\n",
    "\n",
    "sns.barplot(x=\"label\", y=\"accuracy\", hue=\"clf\", palette=blues, data=results)\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Accuracy (%)\", fontsize=32)\n",
    "\n",
    "plt.tick_params(axis=\"x\", labelsize=28)\n",
    "plt.tick_params(axis=\"y\", labelsize=24)\n",
    "\n",
    "plt.legend(loc=\"upper left\", fontsize=28)\n",
    "\n",
    "pylab.ylim([50,100])\n",
    "plt.savefig(\"graphics/sentiment_analyzer_accuracy.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at Most Important Keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'accuracy', u'clf', u'features', u'label'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print results.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def print_top_unigrams(df, clf, label):\n",
    "    print clf, label\n",
    "    \n",
    "    for each in df.ix[(df[\"clf\"]==clf) & (df[\"label\"]==label)][\"features\"].tolist()[0]:\n",
    "        print each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bernoulli NB General Tweets \n",
      "(Count Vectorized)\n",
      "miss\n",
      "just\n",
      "want\n",
      "like\n",
      "sorry\n",
      "time\n",
      "love\n",
      "know\n",
      "really\n",
      "today\n",
      "Bernoulli NB Airline Tweets \n",
      "(Count Vectorized)\n",
      "cancelled\n",
      "service\n",
      "hours\n",
      "customer\n",
      "hold\n",
      "time\n",
      "just\n",
      "delayed\n",
      "help\n",
      "hour\n"
     ]
    }
   ],
   "source": [
    "print_top_unigrams(results, \"Bernoulli NB\", \"General Tweets \\n(Count Vectorized)\")\n",
    "print_top_unigrams(results, \"Bernoulli NB\", \"Airline Tweets \\n(Count Vectorized)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}