twitter_us_airline_sentiment_analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "twitter_us_airline_sentiment_analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/brycemcwilliams/research-papers/blob/master/twitter_us_airline_sentiment_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jaP-CPyF5Ndl",
        "colab_type": "text"
      },
      "source": [
        "#### The purpose of this analysis is to make up a prediction model where we will be able to predict whether a recommendation is positive or negative. In this analysis, we will focus the positive / neutral / negative sentiment of the tweet.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjnXaaLV5Qff",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "2b628ad9-b392-4774-ac34-20c33f0bc9ac"
      },
      "source": [
        "# 1. install and import necessary libraries.\n",
        "\n",
        "!pip install contractions\n",
        "\n",
        "import re, string, unicodedata                          # Import Regex, string and unicodedata.\n",
        "import contractions                                     # Import contractions library.\n",
        "from bs4 import BeautifulSoup                           # Import BeautifulSoup.\n",
        "\n",
        "import numpy as np                                      # Import numpy.\n",
        "import pandas as pd                                     # Import pandas.\n",
        "import nltk                                             # Import Natural Language Tool-Kit.\n",
        "\n",
        "nltk.download('stopwords')                              # Download Stopwords.\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "from nltk.corpus import stopwords                       # Import stopwords.\n",
        "from nltk.tokenize import word_tokenize, sent_tokenize  # Import Tokenizer.\n",
        "from nltk.stem.wordnet import WordNetLemmatizer         # Import Lemmatizer."
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: contractions in /usr/local/lib/python3.6/dist-packages (0.0.25)\n",
            "Requirement already satisfied: textsearch in /usr/local/lib/python3.6/dist-packages (from contractions) (0.0.17)\n",
            "Requirement already satisfied: pyahocorasick in /usr/local/lib/python3.6/dist-packages (from textsearch->contractions) (1.4.0)\n",
            "Requirement already satisfied: Unidecode in /usr/local/lib/python3.6/dist-packages (from textsearch->contractions) (1.1.1)\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H038VPqFQtCz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "244e9023-6fc5-4423-87ad-eacd850ac95f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqIcJMJw7MAE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loading data into pandas dataframe\n",
        "data = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/data/Tweets.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAxBWyO29Qb6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "47c01696-98ad-42d2-83fc-e533dfe45a10"
      },
      "source": [
        "data.shape                                               # print shape of data."
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14640, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LAJWb__77ZNu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "9574133b-fd54-4599-c6b2-b68f7f2682eb"
      },
      "source": [
        "data.head()                                              # Print first 5 rows of data."
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tweet_id</th>\n",
              "      <th>airline_sentiment</th>\n",
              "      <th>airline_sentiment_confidence</th>\n",
              "      <th>negativereason</th>\n",
              "      <th>negativereason_confidence</th>\n",
              "      <th>airline</th>\n",
              "      <th>airline_sentiment_gold</th>\n",
              "      <th>name</th>\n",
              "      <th>negativereason_gold</th>\n",
              "      <th>retweet_count</th>\n",
              "      <th>text</th>\n",
              "      <th>tweet_coord</th>\n",
              "      <th>tweet_created</th>\n",
              "      <th>tweet_location</th>\n",
              "      <th>user_timezone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>570306133677760513</td>\n",
              "      <td>neutral</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>cairdin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica What @dhepburn said.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:35:52 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>570301130888122368</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.3486</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jnardino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:15:59 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>570301083672813571</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.6837</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yvonnalynn</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:15:48 -0800</td>\n",
              "      <td>Lets Play</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>570301031407624196</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Bad Flight</td>\n",
              "      <td>0.7033</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jnardino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:15:36 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>570300817074462722</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Can't Tell</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jnardino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:14:45 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             tweet_id  ...               user_timezone\n",
              "0  570306133677760513  ...  Eastern Time (US & Canada)\n",
              "1  570301130888122368  ...  Pacific Time (US & Canada)\n",
              "2  570301083672813571  ...  Central Time (US & Canada)\n",
              "3  570301031407624196  ...  Pacific Time (US & Canada)\n",
              "4  570300817074462722  ...  Pacific Time (US & Canada)\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4m_zUuoDzu1s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Taking all entries to process.\n",
        "# Only keeping `airline_sentiment` and `text` columns from the data\n",
        "# as these are useful for our analysis.\n",
        "\n",
        "data = data.loc[:, ['airline_sentiment', 'text']]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uB4llYK8nvCV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "07ab0381-c273-4755-d42d-d728cf911405"
      },
      "source": [
        "# The sample dataset is mostly negative which would skew the bias of the model to the\n",
        "# negative class. To combat this, we would implore technequies aimed at the multiplication\n",
        "# of the under represented classes in the dataset.\n",
        "\n",
        "data.airline_sentiment.value_counts()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "negative    9178\n",
              "neutral     3099\n",
              "positive    2363\n",
              "Name: airline_sentiment, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Fi1xVk_0g1v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "10175c9c-570f-4033-f1cf-c1459e160fe0"
      },
      "source": [
        "data.isnull().sum(axis=0)                                # Check for NULL values."
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "airline_sentiment    0\n",
              "text                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YeIScfrX7sCd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "de64c2cf-d7b3-4471-f08c-5c0b80875bf6"
      },
      "source": [
        "pd.set_option('display.max_colwidth', None) # Display full dataframe information (Non-turncated Text column.)\n",
        "\n",
        "data.head(20)                                 # Check first 5 rows of data"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>airline_sentiment</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica What @dhepburn said.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica plus you've added commercials to the experience... tacky.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica I didn't today... Must mean I need to take another trip!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica it's really aggressive to blast obnoxious \"entertainment\" in your guests' faces &amp;amp; they have little recourse</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica and it's a really big bad thing about it</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica seriously would pay $30 a flight for seats that didn't have this playing.\\nit's really the only bad thing about flying VA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica yes, nearly every time I fly VX this “ear worm” won’t go away :)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica Really missed a prime opportunity for Men Without Hats parody, there. https://t.co/mWpG7grEZP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>positive</td>\n",
              "      <td>@virginamerica Well, I didn't…but NOW I DO! :-D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica it was amazing, and arrived an hour early. You're too good to me.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica did you know that suicide is the second leading cause of death among teens 10-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica I &amp;lt;3 pretty graphics. so much better than minimal iconography. :D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica This is such a great deal! Already thinking about my 2nd trip to @Australia &amp;amp; I haven't even gone on my 1st trip yet! ;p</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica @virginmedia I'm flying your #fabulous #Seductive skies again! U take all the #stress away from travel http://t.co/ahlXHhKiyn</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica Thanks!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica SFO-PDX schedule is still MIA.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica So excited for my first cross country flight LAX to MCO I've heard nothing but great things about Virgin America. #29DaysToGo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica  I flew from NYC to SFO last week and couldn't fully sit in my seat due to two large gentleman on either side of me. HELP!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>positive</td>\n",
              "      <td>I ❤️ flying @VirginAmerica. ☺️👍</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica you know what would be amazingly awesome? BOS-FLL PLEASE!!!!!!! I want to fly with only you.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   airline_sentiment                                                                                                                                          text\n",
              "0            neutral                                                                                                           @VirginAmerica What @dhepburn said.\n",
              "1           positive                                                                      @VirginAmerica plus you've added commercials to the experience... tacky.\n",
              "2            neutral                                                                       @VirginAmerica I didn't today... Must mean I need to take another trip!\n",
              "3           negative                @VirginAmerica it's really aggressive to blast obnoxious \"entertainment\" in your guests' faces &amp; they have little recourse\n",
              "4           negative                                                                                       @VirginAmerica and it's a really big bad thing about it\n",
              "5           negative      @VirginAmerica seriously would pay $30 a flight for seats that didn't have this playing.\\nit's really the only bad thing about flying VA\n",
              "6           positive                                                               @VirginAmerica yes, nearly every time I fly VX this “ear worm” won’t go away :)\n",
              "7            neutral                                  @VirginAmerica Really missed a prime opportunity for Men Without Hats parody, there. https://t.co/mWpG7grEZP\n",
              "8           positive                                                                                               @virginamerica Well, I didn't…but NOW I DO! :-D\n",
              "9           positive                                                              @VirginAmerica it was amazing, and arrived an hour early. You're too good to me.\n",
              "10           neutral                                               @VirginAmerica did you know that suicide is the second leading cause of death among teens 10-24\n",
              "11          positive                                                           @VirginAmerica I &lt;3 pretty graphics. so much better than minimal iconography. :D\n",
              "12          positive   @VirginAmerica This is such a great deal! Already thinking about my 2nd trip to @Australia &amp; I haven't even gone on my 1st trip yet! ;p\n",
              "13          positive  @VirginAmerica @virginmedia I'm flying your #fabulous #Seductive skies again! U take all the #stress away from travel http://t.co/ahlXHhKiyn\n",
              "14          positive                                                                                                                        @VirginAmerica Thanks!\n",
              "15          negative                                                                                                 @VirginAmerica SFO-PDX schedule is still MIA.\n",
              "16          positive  @VirginAmerica So excited for my first cross country flight LAX to MCO I've heard nothing but great things about Virgin America. #29DaysToGo\n",
              "17          negative     @VirginAmerica  I flew from NYC to SFO last week and couldn't fully sit in my seat due to two large gentleman on either side of me. HELP!\n",
              "18          positive                                                                                                               I ❤️ flying @VirginAmerica. ☺️👍\n",
              "19          positive                                   @VirginAmerica you know what would be amazingly awesome? BOS-FLL PLEASE!!!!!!! I want to fly with only you."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtWdbYiL79M6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7a90479a-d5bb-454c-fff8-5a10a5a0e7e4"
      },
      "source": [
        "data.shape                                # Shape of data"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14640, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "377IfFic-Ihk",
        "colab_type": "text"
      },
      "source": [
        "### Data Pre-processing:\n",
        "\n",
        "- Remove HTML tags.\n",
        "- Replace contractions in String.\n",
        "- Remove Numbers.\n",
        "- Tokenization\n",
        "- Remove Stopwords.\n",
        "- Lemmatized data\n",
        "\n",
        "We have used NLTK library to tokenize words, remove stopwords and lemmatize the remaining words."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vf3kMpGN7Lp9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "593cbdaa-ca17-465e-ab25-c4e0b2925dd8"
      },
      "source": [
        "def strip_html(text):\n",
        "    soup = BeautifulSoup(text, \"html.parser\")\n",
        "    return soup.get_text()\n",
        "\n",
        "data['text'] = data['text'].apply(lambda x: strip_html(x))\n",
        "data.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>airline_sentiment</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica What @dhepburn said.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica plus you've added commercials to the experience... tacky.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica I didn't today... Must mean I need to take another trip!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica it's really aggressive to blast obnoxious \"entertainment\" in your guests' faces &amp; they have little recourse</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica and it's a really big bad thing about it</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  airline_sentiment                                                                                                                        text\n",
              "0           neutral                                                                                         @VirginAmerica What @dhepburn said.\n",
              "1          positive                                                    @VirginAmerica plus you've added commercials to the experience... tacky.\n",
              "2           neutral                                                     @VirginAmerica I didn't today... Must mean I need to take another trip!\n",
              "3          negative  @VirginAmerica it's really aggressive to blast obnoxious \"entertainment\" in your guests' faces & they have little recourse\n",
              "4          negative                                                                     @VirginAmerica and it's a really big bad thing about it"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7NgnYq77Puy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "58426bb3-f42c-4092-cf9d-d9f415bb25ef"
      },
      "source": [
        "def replace_contractions(text):\n",
        "    \"\"\"Replace contractions in string of text\"\"\"\n",
        "    return contractions.fix(text)\n",
        "\n",
        "data['text'] = data['text'].apply(lambda x: replace_contractions(x))\n",
        "data.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>airline_sentiment</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica What @dhepburn said.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica plus you have added commercials to the experience... tacky.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica I did not today... Must mean I need to take another trip!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica it is really aggressive to blast obnoxious \"entertainment\" in your guests' faces &amp; they have little recourse</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica and it is a really big bad thing about it</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  airline_sentiment                                                                                                                         text\n",
              "0           neutral                                                                                          @VirginAmerica What @dhepburn said.\n",
              "1          positive                                                   @VirginAmerica plus you have added commercials to the experience... tacky.\n",
              "2           neutral                                                     @VirginAmerica I did not today... Must mean I need to take another trip!\n",
              "3          negative  @VirginAmerica it is really aggressive to blast obnoxious \"entertainment\" in your guests' faces & they have little recourse\n",
              "4          negative                                                                     @VirginAmerica and it is a really big bad thing about it"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ga8ArYYxAB_1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "0c33b313-1881-40e5-f4e5-718eb50914cc"
      },
      "source": [
        "def remove_numbers(text):\n",
        "  text = re.sub(r'\\d+', '', text)\n",
        "  return text\n",
        "\n",
        "data['text'] = data['text'].apply(lambda x: remove_numbers(x))\n",
        "data.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>airline_sentiment</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica What @dhepburn said.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica plus you have added commercials to the experience... tacky.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica I did not today... Must mean I need to take another trip!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica it is really aggressive to blast obnoxious \"entertainment\" in your guests' faces &amp; they have little recourse</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica and it is a really big bad thing about it</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  airline_sentiment                                                                                                                         text\n",
              "0           neutral                                                                                          @VirginAmerica What @dhepburn said.\n",
              "1          positive                                                   @VirginAmerica plus you have added commercials to the experience... tacky.\n",
              "2           neutral                                                     @VirginAmerica I did not today... Must mean I need to take another trip!\n",
              "3          negative  @VirginAmerica it is really aggressive to blast obnoxious \"entertainment\" in your guests' faces & they have little recourse\n",
              "4          negative                                                                     @VirginAmerica and it is a really big bad thing about it"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0-yYsx68DxT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['text'] = data.apply(lambda row: nltk.word_tokenize(row['text']), axis=1) # Tokenization of data"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EcHbJJFfAlM2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "86449fa7-e198-4b25-f13d-9846a7fe29db"
      },
      "source": [
        "data.head()                                                                    # Look at how tokenized data looks."
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>airline_sentiment</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>neutral</td>\n",
              "      <td>[@, VirginAmerica, What, @, dhepburn, said, .]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>positive</td>\n",
              "      <td>[@, VirginAmerica, plus, you, have, added, commercials, to, the, experience, ..., tacky, .]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>neutral</td>\n",
              "      <td>[@, VirginAmerica, I, did, not, today, ..., Must, mean, I, need, to, take, another, trip, !]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>negative</td>\n",
              "      <td>[@, VirginAmerica, it, is, really, aggressive, to, blast, obnoxious, ``, entertainment, '', in, your, guests, ', faces, &amp;, they, have, little, recourse]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>negative</td>\n",
              "      <td>[@, VirginAmerica, and, it, is, a, really, big, bad, thing, about, it]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  airline_sentiment                                                                                                                                                      text\n",
              "0           neutral                                                                                                            [@, VirginAmerica, What, @, dhepburn, said, .]\n",
              "1          positive                                                               [@, VirginAmerica, plus, you, have, added, commercials, to, the, experience, ..., tacky, .]\n",
              "2           neutral                                                              [@, VirginAmerica, I, did, not, today, ..., Must, mean, I, need, to, take, another, trip, !]\n",
              "3          negative  [@, VirginAmerica, it, is, really, aggressive, to, blast, obnoxious, ``, entertainment, '', in, your, guests, ', faces, &, they, have, little, recourse]\n",
              "4          negative                                                                                    [@, VirginAmerica, and, it, is, a, really, big, bad, thing, about, it]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWZwuXaC-4qy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stopwords = stopwords.words('english')\n",
        "\n",
        "customlist = ['not', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn',\n",
        "        \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn',\n",
        "        \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn',\n",
        "        \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n",
        "\n",
        "# Set custom stop-word's list as not, couldn't etc. words matter in Sentiment, so not removing them from original data.\n",
        "\n",
        "stopwords = list(set(stopwords) - set(customlist))                              "
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZjCxefg7Et3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8b0ce32f-e228-413c-ec3f-15f1df66717e"
      },
      "source": [
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "def remove_non_ascii(words):\n",
        "    \"\"\"Remove non-ASCII characters from list of tokenized words\"\"\"\n",
        "    new_words = []\n",
        "    for word in words:\n",
        "        new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
        "        new_words.append(new_word)\n",
        "    return new_words\n",
        "\n",
        "def to_lowercase(words):\n",
        "    \"\"\"Convert all characters to lowercase from list of tokenized words\"\"\"\n",
        "    new_words = []\n",
        "    for word in words:\n",
        "        new_word = word.lower()\n",
        "        new_words.append(new_word)\n",
        "    return new_words\n",
        "\n",
        "def remove_punctuation(words):\n",
        "    \"\"\"Remove punctuation from list of tokenized words\"\"\"\n",
        "    new_words = []\n",
        "    for word in words:\n",
        "        new_word = re.sub(r'[^\\w\\s]', '', word)\n",
        "        if new_word != '':\n",
        "            new_words.append(new_word)\n",
        "    return new_words\n",
        "\n",
        "def remove_stopwords(words):\n",
        "    \"\"\"Remove stop words from list of tokenized words\"\"\"\n",
        "    new_words = []\n",
        "    for word in words:\n",
        "        if word not in stopwords:\n",
        "            new_words.append(word)\n",
        "    return new_words\n",
        "\n",
        "def lemmatize_list(words):\n",
        "    new_words = []\n",
        "    for word in words:\n",
        "      new_words.append(lemmatizer.lemmatize(word, pos='v'))\n",
        "    return new_words\n",
        "\n",
        "def normalize(words):\n",
        "    words = remove_non_ascii(words)\n",
        "    words = to_lowercase(words)\n",
        "    words = remove_punctuation(words)\n",
        "    words = remove_stopwords(words)\n",
        "    words = lemmatize_list(words)\n",
        "    return ' '.join(words)\n",
        "\n",
        "data['text'] = data.apply(lambda row: normalize(row['text']), axis=1)\n",
        "data.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>airline_sentiment</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>neutral</td>\n",
              "      <td>virginamerica dhepburn say</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>positive</td>\n",
              "      <td>virginamerica plus add commercials experience tacky</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>neutral</td>\n",
              "      <td>virginamerica not today must mean need take another trip</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>negative</td>\n",
              "      <td>virginamerica really aggressive blast obnoxious entertainment guests face little recourse</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>negative</td>\n",
              "      <td>virginamerica really big bad thing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  airline_sentiment                                                                                       text\n",
              "0           neutral                                                                 virginamerica dhepburn say\n",
              "1          positive                                        virginamerica plus add commercials experience tacky\n",
              "2           neutral                                   virginamerica not today must mean need take another trip\n",
              "3          negative  virginamerica really aggressive blast obnoxious entertainment guests face little recourse\n",
              "4          negative                                                         virginamerica really big bad thing"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JqH_4JWOurH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Vectorization (Convert text data to numbers).\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "vectorizer = CountVectorizer(max_features=1000)                # Keep only 1000 features as number of features will increase the processing time.\n",
        "data_features = vectorizer.fit_transform(data['text'])\n",
        "\n",
        "data_features = data_features.toarray()                        # Convert the data features to array."
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-xSq6Y-YN3f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ce5fcd62-d683-4535-b6ca-91916e12c009"
      },
      "source": [
        "data_features.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14640, 1000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rt_rjzW51fx8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sentiment_dict = { 'negative': 0, 'neutral': 1, 'positive': 2 }\n",
        "labels = data['airline_sentiment'].apply(lambda x: sentiment_dict[x])\n",
        "labels = labels.astype('int')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuBWTlx_ZlRo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split data into training and testing set.\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(data_features, labels, test_size=0.3, random_state=42)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrRKfuzXXyPS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "95c4c70c-729e-449a-8da7-4fd0da48e30e"
      },
      "source": [
        "# Using Random Forest to build model for the classification of tweets.\n",
        "# Also calculating the cross validation score.\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "forest = RandomForestClassifier(n_estimators=10, n_jobs=4)\n",
        "\n",
        "forest = forest.fit(X_train, y_train)\n",
        "\n",
        "print(forest)\n",
        "\n",
        "print(np.mean(cross_val_score(forest, data_features, labels, cv=10)))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=None, max_features='auto',\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=4,\n",
            "                       oob_score=False, random_state=None, verbose=0,\n",
            "                       warm_start=False)\n",
            "0.7139344262295082\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuwujDwkZIW7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predict the result for test data using the model built above.\n",
        "\n",
        "result = forest.predict(X_test)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLRfUCOlaaHH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "5228672c-bf0d-40ca-a289-153aee2205b1"
      },
      "source": [
        "# Print and plot Confusion matirx to get an idea of how the distribution of the prediction is, among all the classes.\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "conf_mat = confusion_matrix(y_test, result)\n",
        "\n",
        "print(conf_mat)\n",
        "\n",
        "df_cm = pd.DataFrame(conf_mat, index = [i for i in \"012\"],\n",
        "                  columns = [i for i in \"012\"])\n",
        "plt.figure(figsize = (10,7))\n",
        "sns.heatmap(df_cm, annot=True, fmt='g')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[2477  254   83]\n",
            " [ 347  457   80]\n",
            " [ 174  118  402]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f322b6154e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JyWqFm4Bu8gX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9be4cd92-360c-455e-fa60-9d4fb45b7050"
      },
      "source": [
        "# Using TfidfVectorizer to convert text data to numbers.\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "vectorizer = TfidfVectorizer(max_features=1000)\n",
        "data_features = vectorizer.fit_transform(data['text'])\n",
        "\n",
        "data_features = data_features.toarray()\n",
        "\n",
        "data_features.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14640, 5000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7223wy58vjJn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "12ff1808-c5fa-4643-b7aa-7567b507261f"
      },
      "source": [
        "# Using Random Forest to build model for the classification of tweets.\n",
        "# Also calculating the cross validation score.\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "forest = RandomForestClassifier(n_estimators=10, n_jobs=4)\n",
        "\n",
        "forest = forest.fit(X_train, y_train)\n",
        "\n",
        "print(forest)\n",
        "\n",
        "print(np.mean(cross_val_score(forest, data_features, labels, cv=10)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=None, max_features='auto',\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=4,\n",
            "                       oob_score=False, random_state=None, verbose=0,\n",
            "                       warm_start=False)\n",
            "0.7136612021857923\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxMaBRnLvl_A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result = forest.predict(X_test)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFkEFqU3vnbw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "cb4cb8c4-fe80-4160-ab59-6d4ad1b18e8b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "conf_mat = confusion_matrix(y_test, result)\n",
        "\n",
        "df_cm = pd.DataFrame(conf_mat, index = [i for i in \"012\"],\n",
        "                  columns = [i for i in \"012\"])\n",
        "plt.figure(figsize = (10,7))\n",
        "sns.heatmap(df_cm, annot=True, fmt='g')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f322b8feb38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAESPCgsAAIGLw6RbEhYAAAIXh2XNtIQAAID3qLAAABC4OFyan4QFAIDA0RICAADwABUWAAACF4frsJCwAAAQuDgsa6YlBAAAvEeFBQCAwLFKCAAAeI85LEiZChUr6MlXH1GFCuWVlp6maa+/rYfvfEKSdOm1F+vEU/+sRG5Cz495WeNGvaBq+1TV7Q8NU72GdZWWnqYxj4zTaxMmR/wp4Jty5crpyTcf0/o1G/S3ftfp+ruv1iFHtJSZacXSVRp++Qhtz96uk8/upsE3DtT6rA2SpBeffEUTx/F9QtEuG3KRLrigj5xzWrToS/W/8Er1v6CPhlx6oZo3b6a69Q/Xxo2bow4TMUHCUkp2/GeHLuw1WNuztys9PU1jJj6m2dPfV7ODmqpew7rqcUxvOedUs3YNSVLv88/UN19/q0vPvVo1alXXxNnPafJLbylnZ07EnwQ++Z8Le2nZkuWqWq2qJOm+oQ8pe2u2JOmyoZfozAtO19MPjpMkTZv4tu6+4f7IYkVYGjSop8GDLtDv/9BJP/74o8aPe1T/c3YPzXl/viZPmabpU1+MOkQUwKRbpNT27O2SpPTy6UpPT5dzTmf3O0OP3j1aLtmA3LQh738rzjlVrVZFklSlamV9/90W5ebkRhM4vLR//f3VoXP7n1RKdiUrklSxUsV4NLZRYtLT01W5ciWlpaWpSuXKWrMmSx9/vFjLl6+KOjT8jHOp23xFwlKKypUrp+enjdE7i6bo/Znz9NnCz9W4SUN169FZ498arYfH3aPfNWskSRo/+kU1a9FU0z+ZpJfefkZ33HhvflIDSNIVNw/Wg7c+Jpf46ffi/+69RlM+eVlNmv9Oz49+OX+800nH6Zlpo3TbyJtVp8H+pR0uArN6dZbuufdRffvNPK1asVDfb9miqdNmRh0WYmyvExYzOz+VgcRBIpHQ2Sf004mteujwVoeq+cEHqELF8vrPf3aoT9cL9NIzr+mWe2+QJHXs1E5fLVqizn84VWd17qfrb7sqv+ICdDzhaG3esFlfffb1L/bdesUdOqXVmVq2ZLlOOK2TJGnW1Dk6vV1v/e8J/TVv5gLddN91pR0yAlO9+n467dSuan5QezVu0lpVq1ZR375nRB0WCpFwlrLNV7+lwnJzYTvMbICZLTCzBZuy1/6GU5RNP2zZqvnvfaSOndpr7er1mj7lHUnS9CnvqsWhzSVJPXqfnD++ctkqZa5YrWYtmkYTMLxzxFGH69guHfXKBxM0/JGb1OaYVhr2rxvy9ycSCU19bYY6nfQnSdKWzVu0c8dOSdLEcZN18BEHRRI3wtG587H6dtkKbdiwSTk5OXrl1Td0dPs2UYeFQjhnKdt8VWTCYmafFrJ9JqluYa9zzo10zrVxzrWpWaXQw2KlRq3q2mffapLy5hYcfdxR+jZjuWa8+a6O6vhHSVKbDq20fOkKSVJW5lq1Ozbvl0PN2jXU5MAmWrU8M5rg4Z1Hbn9cp7U5S6e3660b/3qLFsxeqGGX/kONmjbMP+bYrh21/Ju871OtOjX/O96lg5YtWVHqMSMsK1dkql271qpcuZIk6c+djtGXXy6JOCrEWXGrhOpK6irp5+vWTNKcEomojKpdp5ZufeAmpaWVU7lyprcmztDMqe9p4Qef6PaHh+mcAb2VvS1bw668XZL02D1Pavj9/6eX3n5GZtJ9tz6k7zZ9H/GngM/MTDfdf62qVKsqM1PG5xm649p7JUln9++lY7t0UG5OrrZ894OGXzEi4mjhu3nzF+rllydr/ry3lJOTo48/XqzHn3hWgwddoL9ddYnq1dtfCz+cpjfenKGLB14ddbix53MrJ1WsqImcZjZK0pPOudm72TfOOde3uBMcUe9oZooipaqUqxh1CChDFmygaoDUy9mRWaoZxNwGZ6Tsb2371S97mf0UWWFxzvUvYl+xyQoAACh5caiwsKwZAAB4jyvdAgAQOJ9X96QKCQsAAIFLRB1AKaAlBAAAvEeFBQCAwDnREgIAAJ5LxOACIrSEAACA96iwAAAQuAQtIQAA4Ls4zGGhJQQAALxHhQUAgMDF4TosJCwAAASOlhAAAIAHqLAAABA4WkIAAMB7cUhYaAkBAADvUWEBACBwcZh0S8ICAEDgEmU/X6ElBAAA/EeFBQCAwHEvIQAA4D0XdQClgJYQAADwHhUWAAACF4frsJCwAAAQuISV/TkstIQAAID3qLAAABC4OEy6JWEBACBwcZjDQksIAAB4jwoLAACBi8Ol+UlYAAAIXByudEtLCAAAeI8KCwAAgWOVEAAA8F4c5rDQEgIAAN6jwgIAQOC4DgsAAPCeS+FWHDMbbWbrzGxRgbGaZjbVzJYkf9ZIjpuZPWBmGWb2qZm1LvCafsnjl5hZv+LOS8ICAAB+jackdfvZ2LWSpjvnWkiannwuSd0ltUhuAyQ9IuUlOJKGSmonqa2kobuSnMKQsAAAELiEpW4rjnNupqRNPxvuIWlM8vEYST0LjI91eeZKqm5m9SV1lTTVObfJObdZ0lT9Mgn6CeawAAAQOA/msNR1zq1JPs6SVDf5uKGklQWOW5UcK2y8UFRYAABAPjMbYGYLCmwDfs3rnXN7Oh3mV6HCAgBA4FJZYXHOjZQ08le+bK2Z1XfOrUm2fNYlxzMlNS5wXKPkWKak4382/k5RJ6DCAgBA4JylbttLEyXtWunTT9JrBcbPTa4Wai/p+2Tr6C1JXcysRnKybZfkWKGosAAAgD1mZuOVVx2pbWarlLfaZ4Sk582sv6Tlks5OHj5F0kmSMiRlSzpfkpxzm8xsuKT5yeNucc79fCLvT5CwAAAQuNKcdOuc61PIrs67OdZJGlTI+4yWNHpPz0vCAgBA4DxYJVTimMMCAAC8R4UFAIDApXwNsYdIWAAACNyeXKE2dLSEAACA96iwAAAQuDhMuiVhAQAgcHFIWGgJAQAA71FhAQAgcKwSAgAA3ovDKiESFgAAAsccFgAAAA9QYQEAIHDMYUmB5VvXlfQpEDM7cnOiDgFlyBG1mkUdAvCbJWKQstASAgAA3qMlBABA4OIw6ZaEBQCAwJX9hhAtIQAAEAAqLAAABI6WEAAA8F4crnRLSwgAAHiPCgsAAIGLw3VYSFgAAAhc2U9XaAkBAIAAUGEBACBwrBICAADei8McFlpCAADAe1RYAAAIXNmvr5CwAAAQvDjMYaElBAAAvEeFBQCAwMVh0i0JCwAAgSv76QotIQAAEAAqLAAABC4Ok25JWAAACJyLQVOIlhAAAPAeFRYAAAJHSwgAAHgvDsuaaQkBAADvUWEBACBwZb++QsICAEDwaAkBAAB4gAoLAACBY5UQAADwHheOAwAA8AAVFgAAAkdLCAAAeI+WEAAAgAeosAAAEDhaQgAAwHsJR0sIAAAgclRYAAAIXNmvr5CwAAAQPO4lBAAA4AEqLAAABC4O12EhYQEAIHBxWNZMSwgAAHiPCgsAAIGLw6RbEhYAAAIXhzkstIQAAID3qLAAABC4OEy6JWEBACBwjnsJAQAARI8KCwAAgWOVEAAA8B5zWAAAgPdY1gwAAOABKiwAAASOOSwAAMB7LGsGAADwABUWAAACF4dVQlRYAAAInEvhv+KY2TIz+8zMPjazBcmxmmY21cyWJH/WSI6bmT1gZhlm9qmZtd7bz0jCAgAAfq1OzrkjnXNtks+vlTTdOddC0vTkc0nqLqlFchsg6ZG9PSEJSyl58OERyvh2nt6f90b+2JNjHtCsOZM0a84kfbr4Xc2aM0mS1PqPR+SPz37/dZ1yapeowobHHn30Ti1f/qEWLPh3/thNN12lefPe1Ny5UzRp0tOqX79O/r677x6mRYve1bx5b+rIIw+PImR4rELFCho7ZaQmTHtKL7zztAb+7QJJ0lEdW+vZf4/S82+P1c3336C0tDRJUtPmv9NTkx7V3GUzdM7APlGGDuWtEkrVtpd6SBqTfDxGUs8C42NdnrmSqptZ/b05gZX0zOL9qh1Y9qcu74EOHY/Stq3ZevTxu3R02+6/2H/rbddpy5Yf9M8RD6py5UrasWOncnNzVbfu/npv7mS1bH60cnNzI4jcPztyc6IOwQsdO7bVtm3ZeuKJe9SmTV5Su88+1fTDD1slSZdccp4OPriFhgy5QV27dtJf/3qeevbsp7ZtW+muu4bquON6FvX2sXFI9cZRh+CNylUqa3v2dqWnp2nUa4/o7qEPaMSjN2vg2ZdrxdKVGnh1f61ZlaXXxk9WjVrVVb9RPXXqfpy2fPeDnn50fNThe+WjNbOtNM/XuVGXlP2tnZE59WLlVUN2GemcG7nriZl9K2mzJCfpMefcSDP7zjlXPbnfJG12zlU3s9cljXDOzU7umy7pGufcgl8bFxWWUjLnvfnavPm7QveffsbJevGF1yVJ27f/mJ+cVKpUMRbL1fDrvffePG3a9NPv1K5kRZKqVKmS/9055ZQTNW7cS5KkefMWar/99lW9enUEFLQ9e7skKb18utLLpymRm9DOnTlasXSlJOmDmfPV+eTjJUmbN36nzz/5Ujk7+Q9EWeOcG+mca1NgG/mzQ45xzrVWXrtnkJkd97PXOyn1F4YpNmExs4PNrLOZVfvZeLdUBxNXHToepfXrNmjpN8vyx/7Y5g+aO/8Nzflgiq647EaqK9hjw4ZdrSVL3lfv3j01fPg9kqQGDepp1arV+cdkZmapQYO6UYUIT5UrV07jpz6paZ9N0gfvLtCihZ8rPT1Nh/yhpSSp8ymdVLcBia6PSrMl5JzLTP5cJ+kVSW0lrd3V6kn+XJc8PFNSwTJmo+TYr1ZkwmJmQyS9JulSSYvMrEeB3bcV8boBZrbAzBbs2Lllb+KKlTPPOlUvvjDpJ2MfLvhE7Y/qrk5/Ol1XXjVQFStWiCg6hGbYsDvVosXRmjDhVQ0c2C/qcBCQRCKhPieer26tz9BhrQ7RgS2b6bqBQ/W3m4do7JSRyt6arURuHBbQhqe0VgmZWVUz22fXY0ldJC2SNFHSrl84/ZSXOyg5fm5ytVB7Sd8759bszWcsrsJykaQ/Oud6Sjpe0o1mdtmuuAt7UcFyUoXy++5NXLGRlpamU0/rqpdfmrzb/V9/9Y22bcvWoYe2LOXIELrnnntVPXvmzZdavTpLjRo1yN/XsGE9rV69NqrQ4LmtW7ZqwXsfqUOn9vr0w8Xq33OQzj1pgD6a+7GWJ9tDiK26kmab2SeS5kma7Jx7U9IISSea2RJJJySfS9IUSUslZUh6XNIle3vi4hKWcs65rZLknFumvKSlu5ndoyISFuy54zt11Ndff6PVq7Pyx5o0aZQ/E79x4wZqcdABWr5iVVQhIiAHHtg0//Epp3TR119/I0maPHma+vbtJUlq27aVtmz5QVlZ63b3Foip6rWqq9q+eZ3/ipUqqP2fjtKyjOWqUau6JKl8hfI6b9Bf9NLYV6MME4VIOJeyrSjOuaXOuT8kt8Occ/9Ijm90znV2zrVwzp3gnNuUHHfOuUHOuQOdc7/fm8m2uxR3pdu1Znakc+7j5Im3mtkpkkZL+v3enjSORj15n445tp1q1aqhz7+ardv/cb+eHvuCep15il76WTuo/dFtdMVVF2vnzhy5REJXXTFUmzZujihy+GrMmAd07LFHq3btGsrImKvhw+9Vt26d1KLFAUokElqxIlNDhlwvSXrzzRnq2rWTFi+eqezs7br44r9FHD18s3+dWslly+Vk5cpp6sQZmjVtji6/8RIde2IHmZXTi2Nf0fz3PpIk1dq/pp558wlV3aeqXCKhvhedpTP/9L/atjU74k8ST3FYmlHksmYzayQpxzmXtZt9HZ1z7xV3ApY1I9VY1oxUYlkzSkJpL2s+tmHnlP2tnZU53csOSpEVFudcoX2IPUlWAABAyfsNF3wLBjc/BAAgcHFIWLhwHAAA8B4VFgAAAheHK6KTsAAAEDhaQgAAAB6gwgIAQOCKu6R+WUDCAgBA4OIwh4WWEAAA8B4VFgAAAheHSbckLAAABI6WEAAAgAeosAAAEDhaQgAAwHtxWNZMSwgAAHiPCgsAAIFLxGDSLQkLAACBoyUEAADgASosAAAEjpYQAADwHi0hAAAAD1BhAQAgcLSEAACA92gJAQAAeIAKCwAAgaMlBAAAvEdLCGT/lkoAAAK0SURBVAAAwANUWAAACJxziahDKHEkLAAABC5BSwgAACB6VFgAAAicY5UQAADwHS0hAAAAD1BhAQAgcLSEAACA9+JwpVtaQgAAwHtUWAAACFwcLs1PwgIAQOCYwwIAALzHsmYAAAAPUGEBACBwtIQAAID3WNYMAADgASosAAAEjpYQAADwHquEAAAAPECFBQCAwNESAgAA3mOVEAAAgAeosAAAEDhufggAALxHSwgAAMADVFgAAAgcq4QAAID34jCHhZYQAADwHhUWAAACR0sIAAB4Lw4JCy0hAADgPSosAAAEruzXVySLQxkpFGY2wDk3Muo4UDbwfUKq8Z1ClGgJ+WVA1AGgTOH7hFTjO4XIkLAAAADvkbAAAADvkbD4hd4wUonvE1KN7xQiw6RbAADgPSosAADAeyQsAADAeyQsHjCzbmb2lZllmNm1UceDsJnZaDNbZ2aLoo4FZYOZNTazt83sczNbbGaXRR0T4oc5LBEzszRJX0s6UdIqSfMl9XHOfR5pYAiWmR0naauksc65w6OOB+Ezs/qS6jvnPjKzfSR9KKknv6dQmqiwRK+tpAzn3FLn3A5JEyT1iDgmBMw5N1PSpqjjQNnhnFvjnPso+fgHSV9IahhtVIgbEpboNZS0ssDzVeIXAQBPmVlTSa0kfRBtJIgbEhYAwB4xs2qSXpJ0uXNuS9TxIF5IWKKXKalxgeeNkmMA4A0zK6+8ZOVZ59zLUceD+CFhid58SS3MrJmZVZDUW9LEiGMCgHxmZpJGSfrCOXdP1PEgnkhYIuacy5E0WNJbypvI9rxzbnG0USFkZjZe0vuSWprZKjPrH3VMCF5HSedI+rOZfZzcToo6KMQLy5oBAID3qLAAAADvkbAAAADvkbAAAADvkbAAAADvkbAAAADvkbAAAADvkbAAAADv/T+hUzXaBwk0WAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwxfFdwhc-9o",
        "colab_type": "text"
      },
      "source": [
        "Summary:\n",
        "\n",
        "- We used the dataset which has tweets in text format and their classification as negative / neutral / postive sentiment.\n",
        "- The goal was to build a model for text-classification.\n",
        "- We pre-processed the data using various techniques and libraries.\n",
        "- The pre-precessed data is converted to numbers, so that we can feed the data in the model.\n",
        "- After building the classification model, we predicted the result for the test data.\n",
        "- After that we saw that using the above techniques, our model performed good in perspective of how the text classification models perform.\n",
        "- However, we can still increase the accuracy of our model by increasing the feature size we took into account for the model building.\n",
        "- One more way to increase accuracy is to use different variations of pre-processing techniques."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNKwpQ3Rv6u4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 28,
      "outputs": []
    }
  ]
}