Twitter Airline Sentiment Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "# Twitter Airline Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1136,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' Structure of notebook.\n",
    "Model uses 'bag-of-words approach' with TF-IDF to create a sparse matrix, which connects \n",
    "words to sentiment using Logistic Regression.\n",
    "\n",
    "The predictions from this Logistic Regression are used as features in a Random Forest,\n",
    "in tandem with features engineered (engineered features use knowledge gained from EDA).\n",
    "\n",
    "Accuracy, f1_score, and confusion matrices are used to check strength of models. \n",
    "''';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1084,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_columns = 40\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('Tweets.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Prep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1085,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see how balanced our classes are.\n",
    "sentiment_counts = df['airline_sentiment'].value_counts()\n",
    "plt.bar(sentiment_counts.index.values, height=sentiment_counts );\n",
    "#Not very balanced. Models will skew towards negative. We can weight neutrals and positives in our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1086,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative    0.626913\n",
       "neutral     0.211680\n",
       "positive    0.161407\n",
       "Name: airline_sentiment, dtype: float64"
      ]
     },
     "execution_count": 1086,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#And let's see their ratios.\n",
    "sentiment_ratios = df['airline_sentiment'].value_counts(normalize=True)\n",
    "sentiment_ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1087,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Looking at the proportion of negative/neutral/positive by airline\n",
    "airline_sentiment = pd.crosstab(df['airline'], df['airline_sentiment'])\n",
    "percent=airline_sentiment.apply(lambda group: group / group.sum() * 100, axis=1)\n",
    "percent.plot(kind='bar',figsize=(10, 6),stacked=True,title=\"Airline Sentiment Percentage\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1088,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "What are airline_sentiment_gold and negative_reason_gold? very few values. maybe opinions of gold (vip) flyers?\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1089,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's see how many tweets are in each category\n",
    "pd.Series(df[\"negativereason\"]).value_counts().plot(kind = \"bar\",figsize=(12,6),title = \"Negative Tweets by Reasons\")\n",
    "plt.xlabel('Different Reasons', fontsize=10)\n",
    "plt.ylabel('Tweet Count', fontsize=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1090,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What are the proportions of each individual Airline's problems?\n",
    "\n",
    "df_negative = df.loc[df['airline_sentiment'] == 'negative']         #negative records\n",
    "airline_problems = df_negative.groupby(['airline'])['negativereason'].value_counts(normalize=True)   \n",
    "airline_problems.plot(kind = 'bar',figsize = (20,6),title = 'Problems Ratios by Airline');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1091,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Things we see:\n",
    "\n",
    "1) Most issues are related to Customer Service. \n",
    "This ratio is the worst for American Airlines.\n",
    "\n",
    "2) Delta's largest problem was with their flights being late.\n",
    "\n",
    "3) Virgin America has a problem with their Booking System.\n",
    "Through a Google Search, it looks as though customers often had problems with booking through the website.\n",
    "\n",
    "4) Looks like United Airlines loses luggage at a higher rate than other airlines.\n",
    "\n",
    "\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1092,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking tweets by user.\n",
    "# Good chart. Lets make a graph of this.\n",
    "tweets_by_user = df.groupby(['airline','name'])['airline_sentiment'].value_counts().sort_values(ascending = False)[1:]\n",
    "\n",
    "#plt.plot(tweets_by_user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1093,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Note.\n",
    "Something might not be just positive/negative/neutral. i.e. one tweet talks poorly about United,\n",
    "and says that Southwest is much better. This is negative for one, positive for the other.\n",
    "\n",
    "This is likely where people are unsure of labels (giving a confidence of i.e. .3)\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1094,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts_by_day = df.groupby('tweet_created')['tweet_id'].count().reset_index().dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1138,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will also see how many tweets are made each day. Also, tiemframe is feb 17-24, 2015.\n",
    "\n",
    "counts_by_day = df.groupby('tweet_created')['tweet_id'].count().reset_index().dropna()\n",
    "counts_by_day['tweet_created'] = pd.to_datetime(counts_by_day['tweet_created']).dt.day\n",
    "sns.lineplot(x='tweet_created', y='tweet_id', data=counts_by_day.groupby('tweet_created')['tweet_id'].sum().reset_index());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1096,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Note, there was a weather storm in this part of February, in 2015.\n",
    "Could be that more people tweet about travels during a storm.\n",
    "\n",
    "https://www.weather.gov/media/phi/reports/WSS21Feb2015.pdf\n",
    "''';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drilling into Sentiment Confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1097,
   "metadata": {},
   "outputs": [],
   "source": [
    "#what does sentiment confidence mean?\n",
    "\n",
    "pos_lo_conf = df.loc[\n",
    "    (df['airline_sentiment'] == 'positive') & \n",
    "    (df['airline_sentiment_confidence'] < .5)][['airline_sentiment','airline_sentiment_confidence','text']]\n",
    "\n",
    "#After searching through Kaggle, looks like the confidence is the proportion of people that choose the apparent \n",
    "#sentiment label (positive/negative/neutral). the lower confidence sentiments are mor ambiguous.\n",
    "#Perhaps don't use these for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1098,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''how does confidence map to the original tweets?''';\n",
    "#df[['airline_sentiment','airline_sentiment_confidence','text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1099,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And the tweets that labellers are unsure about?\n",
    "df.loc[df['airline_sentiment_confidence'] < .5][['airline_sentiment','airline_sentiment_confidence','text']];\n",
    "'''\n",
    "These are often ambiguous.\n",
    "We can remove these and only train on labels we're confident about.\n",
    "''';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End of Sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make time a pandas datetime object. so we can work with it.\n",
    "#Note, -800 at end of time may mean gmt -8 hours.\n",
    "df['tweet_created'] = pd.to_datetime(df['tweet_created'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#On a preliminary run-through, lets only work with tweet (labels) we have >.5 confidence in.\n",
    "df = df.loc[df['airline_sentiment_confidence'] > .5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.head(40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove undesirable characters with regular expressions\n",
    "import re\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data has punctuation, numbers. likely not good for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1105,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphanumeric = lambda x: re.sub('\\w*\\d\\w*', ' ', x)                                   # keep only letters\n",
    "punc_lower = lambda x: re.sub('[%s]' % re.escape(string.punctuation), ' ', x.lower()) # keep only lowercase\n",
    "df['text'] = df['text'].map(alphanumeric).map(punc_lower)               #run each tweet through both functions\n",
    "#keep emojis as well.\n",
    "#something like a heart or a frowny face might tell us about sentiment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stemming (To remove suffix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fly, flight, and flying are practically the same word. We will stem our words so that our model doesn't treat these differently.\n",
    "# LancasterStemmer proves to be far too aggressive. Destroys meaning of words. Opt for PorterStemmer instead.\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "porter = PorterStemmer()\n",
    "\n",
    "stemmed_text = []\n",
    "\n",
    "for element in df['text']:                      #Runs through every tweet\n",
    "    tweet_token =  word_tokenize(element)       #tokenizes each word, so they can be stemmed\n",
    "    for i, word in enumerate(tweet_token):\n",
    "        tweet_token[i] = porter.stem(word)      #stemming\n",
    "    stemmed_text.append(' '.join(tweet_token))  #rejoin tweet\n",
    "\n",
    "df['text'] = stemmed_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting our datapoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1107,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['text']\n",
    "Y = df['airline_sentiment']\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X,Y,test_size = .3, random_state = 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End of Data Prep\n",
    "# ------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1108,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#here, we're seeing pos/neut/negative broken down by location.\n",
    "#Airports may not be available, but let's see if we can figure it out from there.\n",
    "dif_cities = df.groupby(['tweet_location'])['airline_sentiment'].value_counts().reset_index(name = 'counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#df.groupby(['tweet_location'])['airline_sentiment'].value_counts().reset_index(name = 'counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF- IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "'''\n",
    "TF-IDF can be effective because it will increase weight of term relevance within document, while reducing \n",
    "based on use in other documents.\n",
    "\n",
    "This will vectorize our documents, but while removing words that are common english, and therefore don't\n",
    "add value to our predictive capability.\n",
    "\n",
    "This will eventually be passed through our classification algorithms.\n",
    "\n",
    "''';\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vectorization portion\n",
    "tfidf = TfidfVectorizer(stop_words='english')\n",
    "x_train_tfidf = tfidf.fit_transform(x_train)\n",
    "x_test_tfidf = tfidf.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics for checking models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "'''\n",
    "F1 score tells a better story than plain accuracy, because it uses precision as well as recall.\n",
    "Confusion Matrix allows us to check our actual category misplacements.\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We're going to use Logistic Regression, because it's good for handling a sparse matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "'''\n",
    "We're going to use Logistic Regression, because it's good for handling a sparse matrix.\n",
    "''';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logistic Regression TF IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ankurkejriwal/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/ankurkejriwal/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7908375751966682"
      ]
     },
     "execution_count": 1116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We'll weight our neutrals and positives, checking against f1.\n",
    "lr_tfidf = LogisticRegression(class_weight={'neutral':4, 'positive':5})\n",
    "lr_tfidf.fit(x_train_tfidf,y_train)\n",
    "lr_tfidf.score(x_test_tfidf,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative: 2695 0.6235539102267469 \n",
      "neutral: 953 0.22049976862563628 \n",
      "positive: 674 0.15594632114761683\n"
     ]
    }
   ],
   "source": [
    "#This is to check the distribution of predictions. We will want to emulate distribution inherent in data.\n",
    "predicts = []\n",
    "predicts = [prediction for prediction in lr_tfidf.predict(x_test_tfidf)]\n",
    "print('negative: {}'.format(predicts.count('negative')), predicts.count('negative')/len(predicts),\n",
    "      '\\n''neutral: {}'.format(predicts.count('neutral')), predicts.count('neutral')/len(predicts),\n",
    "      '\\n''positive: {}'.format(predicts.count('positive')), predicts.count('positive')/len(predicts),\n",
    "      )\n",
    "#originally our models defaulted to predicting negatives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logistic Regression TF-IDF Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1118,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.86695906, 0.61899563, 0.71492537])"
      ]
     },
     "execution_count": 1118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,lr_tfidf.predict(x_test_tfidf),average=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1119,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "This tells us that our model nicely predicts negative reviews, but is a little weaker with the neural and positives.\n",
    "We can explore the actual misclassifications with a confusion matrix.\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2372,  296,  109],\n",
       "       [ 226,  567,   86],\n",
       "       [  97,   90,  479]])"
      ]
     },
     "execution_count": 1120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,lr_tfidf.predict(x_test_tfidf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1121,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "We see from here that most of our misclassified neutral/positive entries, are erroneously labeled as Negative.\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1122,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "We can feature engineer, and add these features to the dataframe. Main issue with this, is that TF_IDF dataframe\n",
    "is a sparse matrix, so anything appended to this risks being drowned out.\n",
    "\n",
    "What we can do instead, is take the prediction probabilities from the 'bag-of-words' approach, which was fed\n",
    "through the original logistic regression, and include these predictions as the the features to another model.\n",
    "The other model, will include features we have engineered.\n",
    "\n",
    "For the other model, we should use a model which can handle non-linear patterns in the features- i.e.\n",
    "Random Forest.\n",
    "''';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacking results with engineered features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1123,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "1) Tweet Length\n",
    "It looks like the negative tweets, seem to be of longer length.\n",
    "\n",
    "2) Day\n",
    "It seems as though there were more negative tweets created around the 21-23 of February.\n",
    "When we explored data, we found that there was a snow storm around this time.\n",
    "\n",
    "''';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Portion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Creating a new dataframe, with the predictions of the logistic regression\n",
    "probabilities_train=pd.DataFrame(lr_tfidf.predict_proba(x_train_tfidf),columns = ['prob_negative','prob_neutral','prob_positive'], index = x_train.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Adding feature 'Tweet_Length'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding length of the tweet\n",
    "probabilities_train['text'] = df.loc[list(x_train.index)]['text']\n",
    "probabilities_train['tweet_length'] = probabilities_train.apply(lambda row: len(row['text']), axis = 1)\n",
    "probabilities_train['airline_sentiment'] = df.loc[list(x_train.index)]['airline_sentiment']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Adding feature 'Day'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding the Day in February\n",
    "probabilities_train['day'] = df.loc[list(x_train.index)]['tweet_created'].dt.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Portion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#populating a dataframe with predictions, using the test set (same as training step above)\n",
    "probabilities_test = pd.DataFrame(lr_tfidf.predict_proba(x_test_tfidf), columns = ['prob_negative','prob_neutral','prob_positive'], index = x_test.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Adding feature 'Tweet_Length'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1128,
   "metadata": {},
   "outputs": [],
   "source": [
    "probabilities_test['text'] = df.loc[list(x_test.index)]['text']\n",
    "probabilities_test['tweet_length'] = probabilities_test.apply(lambda row: len(row['text']), axis = 1)\n",
    "probabilities_test['airline_sentiment'] = df.loc[list(x_test.index)]['airline_sentiment']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Adding feature 'Day'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1129,
   "metadata": {},
   "outputs": [],
   "source": [
    "probabilities_test['day'] = df.loc[list(x_test.index)]['tweet_created'].dt.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1130,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ankurkejriwal/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=100, max_depth=5)\n",
    "features = ['prob_negative','prob_neutral','prob_positive','tweet_length','day']\n",
    "target = ['airline_sentiment']\n",
    "rf.fit(probabilities_train[features],probabilities_train[target]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting Test Set to Random Forest, and checking Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7943081906524757"
      ]
     },
     "execution_count": 1131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy score.\n",
    "rf.score(probabilities_test[features],probabilities_test[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87124845, 0.60487225, 0.70827068])"
      ]
     },
     "execution_count": 1132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is our final result. \n",
    "f1_score(y_test,rf.predict(probabilities_test[features]), average=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2453,  221,  103],\n",
       "       [ 280,  509,   90],\n",
       "       [ 121,   74,  471]])"
      ]
     },
     "execution_count": 1133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,rf.predict(probabilities_test[features]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1134,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Using stacked models and engineered features, We are able to correctly classify roughly\n",
    "80% of tweets.\n",
    "''';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For Future Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1135,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "In order to further develop the model, I would like to:\n",
    "\n",
    "1) Make use of Part-of-Speech Tagging.\n",
    "2) Use transfer learning to learn sentiment better than a \"bag of words\" approach.\n",
    "3) Find meaning using the 'emojis' present in the text.\n",
    "4) See if any particular locations have a relationship with the tags 'negative', 'neutral', and 'positive'\n",
    "\n",
    "''';"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}