Machine_Learning/Twitter Sentiment Analysis/Twitter Sentiment Analysis.ipynb

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    "## Twitter Sentiment Prediction\n",
    "\n",
    "### 1.1 Introduction\n",
    "\n",
    "Taking a look at twitter, there are a lot of tweets generated every single second by different users. In addition, some of these tweets might be positive while the others might be negative. It would be really useful if machine learning and deep learning is deployed in real-time to classify the texts and tweets as positive, negative or neutral respectively. As a result of this, a lot of time and money would be saved by the company without the need to use manpower for these simple tasks respectively. \n",
    "\n",
    "In this machine learning project, we would be working with real-time twitter tweets and predicting the sentiment of the text whether it is positive, negative or neutral. With the help of deep neural networks along with hyperparameter tuning, we would be classifying the tweets, ensuring that there is a good business value for the company by analyzing the text and understanding the working of machine learning models. \n",
    "\n",
    "### 1.2 Metrics\n",
    "\n",
    "1. Accuracy\n",
    "2. Precision \n",
    "3. Recall \n",
    "\n",
    "### 1.3 Source \n",
    "The dataset is taken from Kaggle. It is available in the website below. Feel free to download the dataset. \n",
    "\n",
    "https://www.kaggle.com/c/tweet-sentiment-extraction/data\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "1. Reading the data\n",
    "2. Countplot of the Sentiment\n",
    "3. Positive Text WordCloud\n",
    "4. Negative Text WordCloud\n",
    "5. List of Stopwords\n",
    "6. Dividing the data into training and Cross Validation Data\n",
    "7. Function for Replacing Words\n",
    "8. Preprocessing the Text\n",
    "9. Tfidf Vectorizer\n",
    "10. Neural Network Model for Prediction \n",
    "11. Count Vectorizer\n",
    "12. Neural Network Model \n",
    "13. Plots of the Results \n",
    "14. Conclusion\n",
    "\n",
    "\n",
    "Let us now start the project by reading some useful libraries that we would be working with from the start. "
   ]
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     "text": [
      "Requirement already satisfied: wordcloud in /opt/conda/lib/python3.7/site-packages (1.8.1)\n",
      "Requirement already satisfied: pillow in /opt/conda/lib/python3.7/site-packages (from wordcloud) (7.2.0)\n",
      "Requirement already satisfied: numpy>=1.6.1 in /opt/conda/lib/python3.7/site-packages (from wordcloud) (1.19.5)\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from wordcloud) (3.4.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->wordcloud) (0.10.0)\n",
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      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from cycler>=0.10->matplotlib->wordcloud) (1.15.0)\n"
     ]
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   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.models import Sequential\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "!pip install wordcloud"
   ]
  },
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   "source": [
    "## 1. Reading the data \n",
    "\n",
    "We would be first reading the data that is present in the kaggle repository to ensure that we work with it. "
   ]
  },
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   "source": [
    "df = pd.read_csv('../input/training-data/train.csv')"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the head of the dataframe, we see that there are different columns such as textID, text, selected_text and the output that we are going to be predict which is sentiment respectively. "
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>textID</th>\n",
       "      <th>text</th>\n",
       "      <th>selected_text</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cb774db0d1</td>\n",
       "      <td>I`d have responded, if I were going</td>\n",
       "      <td>I`d have responded, if I were going</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>549e992a42</td>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>Sooo SAD</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>088c60f138</td>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>bullying me</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9642c003ef</td>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>leave me alone</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>358bd9e861</td>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>Sons of ****,</td>\n",
       "      <td>negative</td>\n",
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       "       textID                                               text  \\\n",
       "0  cb774db0d1                I`d have responded, if I were going   \n",
       "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!   \n",
       "2  088c60f138                          my boss is bullying me...   \n",
       "3  9642c003ef                     what interview! leave me alone   \n",
       "4  358bd9e861   Sons of ****, why couldn`t they put them on t...   \n",
       "\n",
       "                         selected_text sentiment  \n",
       "0  I`d have responded, if I were going   neutral  \n",
       "1                             Sooo SAD  negative  \n",
       "2                          bullying me  negative  \n",
       "3                       leave me alone  negative  \n",
       "4                        Sons of ****,  negative  "
      ]
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     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would be dropping the selected text as the test set does not contain those values. It would be good to leave only the text for the machine learning models for predictions respectively. "
   ]
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   "source": [
    "df.drop(['selected_text'], axis = 1, inplace = True)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have removed the selected text, we are only left with the actual text values along with the sentiment whether it is positive, negative or neutral output respectively."
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>textID</th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment</th>\n",
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       "      <th>0</th>\n",
       "      <td>cb774db0d1</td>\n",
       "      <td>I`d have responded, if I were going</td>\n",
       "      <td>neutral</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>549e992a42</td>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>088c60f138</td>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9642c003ef</td>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>negative</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>358bd9e861</td>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>negative</td>\n",
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      "text/plain": [
       "       textID                                               text sentiment\n",
       "0  cb774db0d1                I`d have responded, if I were going   neutral\n",
       "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!  negative\n",
       "2  088c60f138                          my boss is bullying me...  negative\n",
       "3  9642c003ef                     what interview! leave me alone  negative\n",
       "4  358bd9e861   Sons of ****, why couldn`t they put them on t...  negative"
      ]
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   "source": [
    "df.head()"
   ]
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   "source": [
    "## 2. Countplot of the Sentiment \n",
    "\n",
    "As can be seen, there seems to be more neutral sentiment in the text compared to positive or negative tweets. That is true in real life as most of the tweets would be quite neutral without them being either too positive or too negative respectively. "
   ]
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     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'sentiment', data = df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking a look at the shape of the data, we see that there are about 27k data points that we are going to be working and applying our knowledge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.331474Z",
     "iopub.status.busy": "2021-06-21T19:00:13.331069Z",
     "iopub.status.idle": "2021-06-21T19:00:13.337548Z",
     "shell.execute_reply": "2021-06-21T19:00:13.336559Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.331433Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27481, 3)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading a few lines in a particular row of the text, we see that it is quite a simple tweet without being too lengthy.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.340960Z",
     "iopub.status.busy": "2021-06-21T19:00:13.340595Z",
     "iopub.status.idle": "2021-06-21T19:00:13.348820Z",
     "shell.execute_reply": "2021-06-21T19:00:13.347779Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.340923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' I`d have responded, if I were going'"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking a look at the total number of characters from a single text, we see that there are about 36 characters respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.351702Z",
     "iopub.status.busy": "2021-06-21T19:00:13.351229Z",
     "iopub.status.idle": "2021-06-21T19:00:13.359034Z",
     "shell.execute_reply": "2021-06-21T19:00:13.357956Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.351664Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['text'].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now read the head of the text and see the values that are present to understand the working of the machine learning models respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.360952Z",
     "iopub.status.busy": "2021-06-21T19:00:13.360577Z",
     "iopub.status.idle": "2021-06-21T19:00:13.373248Z",
     "shell.execute_reply": "2021-06-21T19:00:13.372473Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.360915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  I`d have responded, if I were going\n",
       "1        Sooo SAD I will miss you here in San Diego!!!\n",
       "2                            my boss is bullying me...\n",
       "3                       what interview! leave me alone\n",
       "4     Sons of ****, why couldn`t they put them on t...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the information present in our dataframe, we can see that almost all the values are not-null except a few that we are going to be clearing to ensure that those values don't interfere in the machine learning models and predictions respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.375954Z",
     "iopub.status.busy": "2021-06-21T19:00:13.375361Z",
     "iopub.status.idle": "2021-06-21T19:00:13.394563Z",
     "shell.execute_reply": "2021-06-21T19:00:13.393396Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.375916Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27481 entries, 0 to 27480\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   textID     27481 non-null  object\n",
      " 1   text       27480 non-null  object\n",
      " 2   sentiment  27481 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 644.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.396526Z",
     "iopub.status.busy": "2021-06-21T19:00:13.396167Z",
     "iopub.status.idle": "2021-06-21T19:00:13.402075Z",
     "shell.execute_reply": "2021-06-21T19:00:13.401160Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.396491Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' I`d have responded, if I were going'"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.404260Z",
     "iopub.status.busy": "2021-06-21T19:00:13.403579Z",
     "iopub.status.idle": "2021-06-21T19:00:13.706277Z",
     "shell.execute_reply": "2021-06-21T19:00:13.705495Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.404217Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n"
     ]
    }
   ],
   "source": [
    "text_length_list = []\n",
    "for i in range(len(df)):\n",
    "    if isinstance(df['text'].iloc[i], float) == True:\n",
    "        print(df['text'].iloc[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.707927Z",
     "iopub.status.busy": "2021-06-21T19:00:13.707576Z",
     "iopub.status.idle": "2021-06-21T19:00:13.713870Z",
     "shell.execute_reply": "2021-06-21T19:00:13.712873Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.707889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(\"suhas\", float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.715636Z",
     "iopub.status.busy": "2021-06-21T19:00:13.715252Z",
     "iopub.status.idle": "2021-06-21T19:00:13.735130Z",
     "shell.execute_reply": "2021-06-21T19:00:13.734085Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.715597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27481 entries, 0 to 27480\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   textID     27481 non-null  object\n",
      " 1   text       27480 non-null  object\n",
      " 2   sentiment  27481 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 644.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.736947Z",
     "iopub.status.busy": "2021-06-21T19:00:13.736307Z",
     "iopub.status.idle": "2021-06-21T19:00:13.751374Z",
     "shell.execute_reply": "2021-06-21T19:00:13.750576Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.736906Z"
    }
   },
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.753549Z",
     "iopub.status.busy": "2021-06-21T19:00:13.752833Z",
     "iopub.status.idle": "2021-06-21T19:00:13.771041Z",
     "shell.execute_reply": "2021-06-21T19:00:13.770293Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.753513Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27480 entries, 0 to 27480\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   textID     27480 non-null  object\n",
      " 1   text       27480 non-null  object\n",
      " 2   sentiment  27480 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 858.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.772705Z",
     "iopub.status.busy": "2021-06-21T19:00:13.772281Z",
     "iopub.status.idle": "2021-06-21T19:00:13.793450Z",
     "shell.execute_reply": "2021-06-21T19:00:13.792656Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.772665Z"
    }
   },
   "outputs": [],
   "source": [
    "df['text_length'] = df['text'].apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.795193Z",
     "iopub.status.busy": "2021-06-21T19:00:13.794788Z",
     "iopub.status.idle": "2021-06-21T19:00:13.805623Z",
     "shell.execute_reply": "2021-06-21T19:00:13.804565Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.795116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <td>549e992a42</td>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>negative</td>\n",
       "      <td>46</td>\n",
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       "      <td>088c60f138</td>\n",
       "      <td>my boss is bullying me...</td>\n",
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       "      <td>25</td>\n",
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       "      <th>3</th>\n",
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       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       textID                                               text sentiment  \\\n",
       "0  cb774db0d1                I`d have responded, if I were going   neutral   \n",
       "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!  negative   \n",
       "2  088c60f138                          my boss is bullying me...  negative   \n",
       "3  9642c003ef                     what interview! leave me alone  negative   \n",
       "4  358bd9e861   Sons of ****, why couldn`t they put them on t...  negative   \n",
       "\n",
       "   text_length  \n",
       "0           36  \n",
       "1           46  \n",
       "2           25  \n",
       "3           31  \n",
       "4           75  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.807637Z",
     "iopub.status.busy": "2021-06-21T19:00:13.807212Z",
     "iopub.status.idle": "2021-06-21T19:00:13.849300Z",
     "shell.execute_reply": "2021-06-21T19:00:13.848538Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.807601Z"
    }
   },
   "outputs": [],
   "source": [
    "df['text_words'] = df['text'].apply(lambda x: len(x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.850837Z",
     "iopub.status.busy": "2021-06-21T19:00:13.850479Z",
     "iopub.status.idle": "2021-06-21T19:00:13.861376Z",
     "shell.execute_reply": "2021-06-21T19:00:13.860454Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.850800Z"
    }
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "       textID                                               text sentiment  \\\n",
       "0  cb774db0d1                I`d have responded, if I were going   neutral   \n",
       "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!  negative   \n",
       "2  088c60f138                          my boss is bullying me...  negative   \n",
       "3  9642c003ef                     what interview! leave me alone  negative   \n",
       "4  358bd9e861   Sons of ****, why couldn`t they put them on t...  negative   \n",
       "\n",
       "   text_length  text_words  \n",
       "0           36           7  \n",
       "1           46          10  \n",
       "2           25           5  \n",
       "3           31           5  \n",
       "4           75          14  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.863628Z",
     "iopub.status.busy": "2021-06-21T19:00:13.862971Z",
     "iopub.status.idle": "2021-06-21T19:00:13.877362Z",
     "shell.execute_reply": "2021-06-21T19:00:13.876370Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.863592Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>negative</td>\n",
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       "      <td>5</td>\n",
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "       textID                                               text sentiment  \\\n",
       "0  cb774db0d1                I`d have responded, if I were going   neutral   \n",
       "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!  negative   \n",
       "2  088c60f138                          my boss is bullying me...  negative   \n",
       "3  9642c003ef                     what interview! leave me alone  negative   \n",
       "4  358bd9e861   Sons of ****, why couldn`t they put them on t...  negative   \n",
       "\n",
       "   text_length  text_words  \n",
       "0           36           7  \n",
       "1           46          10  \n",
       "2           25           5  \n",
       "3           31           5  \n",
       "4           75          14  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.883536Z",
     "iopub.status.busy": "2021-06-21T19:00:13.883256Z",
     "iopub.status.idle": "2021-06-21T19:00:13.904970Z",
     "shell.execute_reply": "2021-06-21T19:00:13.904200Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.883511Z"
    }
   },
   "outputs": [],
   "source": [
    "## Taking separate dataframes for different values such as positive, negative and neutral reviews respectively. \n",
    "\n",
    "positive_df = df[df['sentiment'] == 'positive']\n",
    "negative_df = df[df['sentiment'] == 'negative']\n",
    "neutral_df = df[df['sentiment'] == 'neutral']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.907941Z",
     "iopub.status.busy": "2021-06-21T19:00:13.907592Z",
     "iopub.status.idle": "2021-06-21T19:00:13.913226Z",
     "shell.execute_reply": "2021-06-21T19:00:13.912298Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.907914Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the dataframe that contains only the positive reviews is: (8582, 5)\n",
      "The shape of the dataframe that contains only the negative reviews is: (7781, 5)\n",
      "The shape of the dataframe that contains only the neutral reviews is:  (11117, 5)\n"
     ]
    }
   ],
   "source": [
    "print(\"The shape of the dataframe that contains only the positive reviews is: {}\".format(positive_df.shape))\n",
    "print(\"The shape of the dataframe that contains only the negative reviews is: {}\".format(negative_df.shape))\n",
    "print(\"The shape of the dataframe that contains only the neutral reviews is:  {}\".format(neutral_df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.915330Z",
     "iopub.status.busy": "2021-06-21T19:00:13.914713Z",
     "iopub.status.idle": "2021-06-21T19:00:13.922607Z",
     "shell.execute_reply": "2021-06-21T19:00:13.921622Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.915290Z"
    }
   },
   "outputs": [],
   "source": [
    "wordcloud = WordCloud(width = 500, height = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.924482Z",
     "iopub.status.busy": "2021-06-21T19:00:13.924029Z",
     "iopub.status.idle": "2021-06-21T19:00:13.937959Z",
     "shell.execute_reply": "2021-06-21T19:00:13.936936Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.924442Z"
    }
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>cb774db0d1</td>\n",
       "      <td>I`d have responded, if I were going</td>\n",
       "      <td>neutral</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>549e992a42</td>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>negative</td>\n",
       "      <td>46</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>088c60f138</td>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>negative</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9642c003ef</td>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>negative</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>358bd9e861</td>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>negative</td>\n",
       "      <td>75</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       textID                                               text sentiment  \\\n",
       "0  cb774db0d1                I`d have responded, if I were going   neutral   \n",
       "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!  negative   \n",
       "2  088c60f138                          my boss is bullying me...  negative   \n",
       "3  9642c003ef                     what interview! leave me alone  negative   \n",
       "4  358bd9e861   Sons of ****, why couldn`t they put them on t...  negative   \n",
       "\n",
       "   text_length  text_words  \n",
       "0           36           7  \n",
       "1           46          10  \n",
       "2           25           5  \n",
       "3           31           5  \n",
       "4           75          14  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.939596Z",
     "iopub.status.busy": "2021-06-21T19:00:13.939221Z",
     "iopub.status.idle": "2021-06-21T19:00:13.953096Z",
     "shell.execute_reply": "2021-06-21T19:00:13.951979Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.939559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>textID</th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text_length</th>\n",
       "      <th>text_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6e0c6d75b1</td>\n",
       "      <td>2am feedings for the baby are fun when he is a...</td>\n",
       "      <td>positive</td>\n",
       "      <td>64</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fc2cbefa9d</td>\n",
       "      <td>Journey!? Wow... u just became cooler.  hehe....</td>\n",
       "      <td>positive</td>\n",
       "      <td>69</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16fab9f95b</td>\n",
       "      <td>I really really like the song Love Story by Ta...</td>\n",
       "      <td>positive</td>\n",
       "      <td>56</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>e48b0b8a23</td>\n",
       "      <td>Playing Ghost Online is really interesting. Th...</td>\n",
       "      <td>positive</td>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>e00c6ef376</td>\n",
       "      <td>the free fillin` app on my ipod is fun, im add...</td>\n",
       "      <td>positive</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        textID                                               text sentiment  \\\n",
       "6   6e0c6d75b1  2am feedings for the baby are fun when he is a...  positive   \n",
       "9   fc2cbefa9d   Journey!? Wow... u just became cooler.  hehe....  positive   \n",
       "11  16fab9f95b  I really really like the song Love Story by Ta...  positive   \n",
       "21  e48b0b8a23  Playing Ghost Online is really interesting. Th...  positive   \n",
       "25  e00c6ef376  the free fillin` app on my ipod is fun, im add...  positive   \n",
       "\n",
       "    text_length  text_words  \n",
       "6            64          14  \n",
       "9            69          10  \n",
       "11           56          11  \n",
       "21          135          24  \n",
       "25           51          11  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:13.954857Z",
     "iopub.status.busy": "2021-06-21T19:00:13.954479Z",
     "iopub.status.idle": "2021-06-21T19:00:14.059366Z",
     "shell.execute_reply": "2021-06-21T19:00:14.058470Z",
     "shell.execute_reply.started": "2021-06-21T19:00:13.954820Z"
    }
   },
   "outputs": [],
   "source": [
    "positive_text = []\n",
    "for i in range(len(positive_df)):\n",
    "    positive_text.append(positive_df['text'].iloc[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:14.061769Z",
     "iopub.status.busy": "2021-06-21T19:00:14.061129Z",
     "iopub.status.idle": "2021-06-21T19:00:14.067892Z",
     "shell.execute_reply": "2021-06-21T19:00:14.066984Z",
     "shell.execute_reply.started": "2021-06-21T19:00:14.061728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2am feedings for the baby are fun when he is all smiles and coos',\n",
       " ' Journey!? Wow... u just became cooler.  hehe... (is that possible!?)',\n",
       " 'I really really like the song Love Story by Taylor Swift',\n",
       " 'Playing Ghost Online is really interesting. The new updates are Kirin pet and Metamorph for third job.  Can`t wait to have a dragon pet',\n",
       " 'the free fillin` app on my ipod is fun, im addicted']"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_text[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Positive Text WordCloud \n",
    "\n",
    "Worcloud gives us a good idea about the number of repeating words by the size of them. We see that there are some positive words such as \"thank\" and \"good \"which make up most of the positive reviews. There are also some words that occur quite rare such as \"awesome\" but these words also make a mark in the decisions respectively. In addition to this, we also find that there are very few words that are negative in the positive text. Therefore, we can conclude that words are a good indication of the polarity and sentiment of the text respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:14.069849Z",
     "iopub.status.busy": "2021-06-21T19:00:14.069459Z",
     "iopub.status.idle": "2021-06-21T19:00:15.062416Z",
     "shell.execute_reply": "2021-06-21T19:00:15.061425Z",
     "shell.execute_reply.started": "2021-06-21T19:00:14.069809Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(stopwords = stopwords)\n",
    "wordcloud.generate(''.join(positive_text))\n",
    "plt.figure(figsize = (10, 10))\n",
    "plt.imshow(wordcloud)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:15.064571Z",
     "iopub.status.busy": "2021-06-21T19:00:15.063982Z",
     "iopub.status.idle": "2021-06-21T19:00:15.158866Z",
     "shell.execute_reply": "2021-06-21T19:00:15.158068Z",
     "shell.execute_reply.started": "2021-06-21T19:00:15.064521Z"
    }
   },
   "outputs": [],
   "source": [
    "negative_text = []\n",
    "for i in range(len(negative_df)):\n",
    "    negative_text.append(negative_df['text'].iloc[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:15.160861Z",
     "iopub.status.busy": "2021-06-21T19:00:15.160281Z",
     "iopub.status.idle": "2021-06-21T19:00:15.166578Z",
     "shell.execute_reply": "2021-06-21T19:00:15.165435Z",
     "shell.execute_reply.started": "2021-06-21T19:00:15.160821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Sooo SAD I will miss you here in San Diego!!!',\n",
       " 'my boss is bullying me...',\n",
       " ' what interview! leave me alone',\n",
       " ' Sons of ****, why couldn`t they put them on the releases we already bought',\n",
       " 'My Sharpie is running DANGERously low on ink']"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_text[0: 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Negative Text WordCloud \n",
    "\n",
    "As can be seen in the wordcloud, there are some words such as \"miss\" and \"no\" which are considered to be negative respectively. In addition to this, we see that there are some words such as \"work\" and \"sorry\" that also tend to be negative as this is quite true in real-life where we say \"sorry\" for some negative things done and so on. Therefore, getting the wordcloud would ensure that we get to know the words present in the wordcloud dictionary and ensure that we understand the full context of the review respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:15.168640Z",
     "iopub.status.busy": "2021-06-21T19:00:15.168080Z",
     "iopub.status.idle": "2021-06-21T19:00:16.094523Z",
     "shell.execute_reply": "2021-06-21T19:00:16.093656Z",
     "shell.execute_reply.started": "2021-06-21T19:00:15.168601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(stopwords = stopwords, background_color = 'white')\n",
    "wordcloud.generate(''.join(negative_text))\n",
    "plt.figure(figsize = (10, 10))\n",
    "plt.imshow(wordcloud)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:16.096242Z",
     "iopub.status.busy": "2021-06-21T19:00:16.095812Z",
     "iopub.status.idle": "2021-06-21T19:00:16.110328Z",
     "shell.execute_reply": "2021-06-21T19:00:16.109275Z",
     "shell.execute_reply.started": "2021-06-21T19:00:16.096209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>textID</th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text_length</th>\n",
       "      <th>text_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cb774db0d1</td>\n",
       "      <td>I`d have responded, if I were going</td>\n",
       "      <td>neutral</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>549e992a42</td>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>negative</td>\n",
       "      <td>46</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>088c60f138</td>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>negative</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9642c003ef</td>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>negative</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>358bd9e861</td>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>negative</td>\n",
       "      <td>75</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       textID                                               text sentiment  \\\n",
       "0  cb774db0d1                I`d have responded, if I were going   neutral   \n",
       "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!  negative   \n",
       "2  088c60f138                          my boss is bullying me...  negative   \n",
       "3  9642c003ef                     what interview! leave me alone  negative   \n",
       "4  358bd9e861   Sons of ****, why couldn`t they put them on t...  negative   \n",
       "\n",
       "   text_length  text_words  \n",
       "0           36           7  \n",
       "1           46          10  \n",
       "2           25           5  \n",
       "3           31           5  \n",
       "4           75          14  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:16.112122Z",
     "iopub.status.busy": "2021-06-21T19:00:16.111725Z",
     "iopub.status.idle": "2021-06-21T19:00:16.126399Z",
     "shell.execute_reply": "2021-06-21T19:00:16.125209Z",
     "shell.execute_reply.started": "2021-06-21T19:00:16.112078Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>549e992a42</td>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>negative</td>\n",
       "      <td>46</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>088c60f138</td>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>negative</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9642c003ef</td>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>negative</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>358bd9e861</td>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>negative</td>\n",
       "      <td>75</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>74a76f6e0a</td>\n",
       "      <td>My Sharpie is running DANGERously low on ink</td>\n",
       "      <td>negative</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        textID                                               text sentiment  \\\n",
       "1   549e992a42      Sooo SAD I will miss you here in San Diego!!!  negative   \n",
       "2   088c60f138                          my boss is bullying me...  negative   \n",
       "3   9642c003ef                     what interview! leave me alone  negative   \n",
       "4   358bd9e861   Sons of ****, why couldn`t they put them on t...  negative   \n",
       "12  74a76f6e0a       My Sharpie is running DANGERously low on ink  negative   \n",
       "\n",
       "    text_length  text_words  \n",
       "1            46          10  \n",
       "2            25           5  \n",
       "3            31           5  \n",
       "4            75          14  \n",
       "12           44           8  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_df.head()"
   ]
  },
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    {
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       "      <th>6</th>\n",
       "      <td>6e0c6d75b1</td>\n",
       "      <td>2am feedings for the baby are fun when he is a...</td>\n",
       "      <td>positive</td>\n",
       "      <td>64</td>\n",
       "      <td>14</td>\n",
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       "      <th>9</th>\n",
       "      <td>fc2cbefa9d</td>\n",
       "      <td>Journey!? Wow... u just became cooler.  hehe....</td>\n",
       "      <td>positive</td>\n",
       "      <td>69</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16fab9f95b</td>\n",
       "      <td>I really really like the song Love Story by Ta...</td>\n",
       "      <td>positive</td>\n",
       "      <td>56</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>e48b0b8a23</td>\n",
       "      <td>Playing Ghost Online is really interesting. Th...</td>\n",
       "      <td>positive</td>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>e00c6ef376</td>\n",
       "      <td>the free fillin` app on my ipod is fun, im add...</td>\n",
       "      <td>positive</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        textID                                               text sentiment  \\\n",
       "6   6e0c6d75b1  2am feedings for the baby are fun when he is a...  positive   \n",
       "9   fc2cbefa9d   Journey!? Wow... u just became cooler.  hehe....  positive   \n",
       "11  16fab9f95b  I really really like the song Love Story by Ta...  positive   \n",
       "21  e48b0b8a23  Playing Ghost Online is really interesting. Th...  positive   \n",
       "25  e00c6ef376  the free fillin` app on my ipod is fun, im add...  positive   \n",
       "\n",
       "    text_length  text_words  \n",
       "6            64          14  \n",
       "9            69          10  \n",
       "11           56          11  \n",
       "21          135          24  \n",
       "25           51          11  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. List of Stopwords\n",
    "\n",
    "Let us now make a list of all the stopwords that we are going to be using for our machine learning purposes. We see that there are some good list of stopwords that I have taken from the link that is provided in the below cell. These would be used for the stopwords and ensure that we are getting the best results respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "execution": {
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   "source": [
    "# https://gist.github.com/sebleier/554280\n",
    "# we are removing the words from the stop words list: 'no', 'nor', 'not'\n",
    "stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\",\\\n",
    "            \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \\\n",
    "            'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their',\\\n",
    "            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', \\\n",
    "            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \\\n",
    "            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \\\n",
    "            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\\\n",
    "            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\\\n",
    "            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\\\n",
    "            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \\\n",
    "            's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', \\\n",
    "            've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn',\\\n",
    "            \"hadn't\", 'hasn', \"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', \"wasn't\", 'weren', \"weren't\", \\\n",
    "            'won', \"won't\", 'wouldn', \"wouldn't\"]"
   ]
  },
  {
   "cell_type": "code",
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    {
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       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>negative</td>\n",
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       "      <td>10</td>\n",
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       "      <th>2</th>\n",
       "      <td>088c60f138</td>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>negative</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
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       "      <th>3</th>\n",
       "      <td>9642c003ef</td>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>negative</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>358bd9e861</td>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>negative</td>\n",
       "      <td>75</td>\n",
       "      <td>14</td>\n",
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      "text/plain": [
       "       textID                                               text sentiment  \\\n",
       "0  cb774db0d1                I`d have responded, if I were going   neutral   \n",
       "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!  negative   \n",
       "2  088c60f138                          my boss is bullying me...  negative   \n",
       "3  9642c003ef                     what interview! leave me alone  negative   \n",
       "4  358bd9e861   Sons of ****, why couldn`t they put them on t...  negative   \n",
       "\n",
       "   text_length  text_words  \n",
       "0           36           7  \n",
       "1           46          10  \n",
       "2           25           5  \n",
       "3           31           5  \n",
       "4           75          14  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-06-21T19:00:16.250634Z"
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   },
   "outputs": [],
   "source": [
    "df.drop(['textID'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "execution": {
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    {
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       "      <th>1</th>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>negative</td>\n",
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       "      <td>10</td>\n",
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       "      <th>2</th>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>negative</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>negative</td>\n",
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       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>negative</td>\n",
       "      <td>75</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                                text sentiment  text_length  \\\n",
       "0                I`d have responded, if I were going   neutral           36   \n",
       "1      Sooo SAD I will miss you here in San Diego!!!  negative           46   \n",
       "2                          my boss is bullying me...  negative           25   \n",
       "3                     what interview! leave me alone  negative           31   \n",
       "4   Sons of ****, why couldn`t they put them on t...  negative           75   \n",
       "\n",
       "   text_words  \n",
       "0           7  \n",
       "1          10  \n",
       "2           5  \n",
       "3           5  \n",
       "4          14  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:16.277418Z",
     "iopub.status.busy": "2021-06-21T19:00:16.276921Z",
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     "shell.execute_reply": "2021-06-21T19:00:16.285040Z",
     "shell.execute_reply.started": "2021-06-21T19:00:16.277375Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df.drop(['sentiment'], axis = 1)\n",
    "y = df['sentiment']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Dividing the Data into Training and Cross Validation Data \n",
    "\n",
    "Now we are going to be dividing the data into training and cross validation data and ensure that we understand the machine learning model well. We are going to be dividing the data into 2 parts where 70 percent of the data is taken as the training data and the 30 percent of the data would be taken as the test data. There is a random state assigned to it and split to ensure that we get a good accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-06-21T19:00:16.289785Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_cv, y_train, y_cv = train_test_split(X, y, test_size = 0.3, random_state = 50)"
   ]
  },
  {
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       "(19236, 3)"
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    "X_train.shape"
   ]
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      "text/plain": [
       "(8244, 3)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Function for Replacing Words\n",
    "\n",
    "There is a separate function that is being used to replace the words and substitute them with the other words to ensure that we get the best machine learning results respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:16.335707Z",
     "iopub.status.busy": "2021-06-21T19:00:16.335252Z",
     "iopub.status.idle": "2021-06-21T19:00:16.343763Z",
     "shell.execute_reply": "2021-06-21T19:00:16.342660Z",
     "shell.execute_reply.started": "2021-06-21T19:00:16.335667Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "# https://stackoverflow.com/a/47091490/4084039\n",
    "import re\n",
    "\n",
    "def decontracted(phrase):\n",
    "    # specific\n",
    "    phrase = re.sub(r\"won't\", \"will not\", phrase)\n",
    "    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n",
    "\n",
    "    # general\n",
    "    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n",
    "    phrase = re.sub(r\"\\'s\", \" is\", phrase)\n",
    "    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n",
    "    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n",
    "    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n",
    "    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n",
    "    return phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:16.345879Z",
     "iopub.status.busy": "2021-06-21T19:00:16.345339Z",
     "iopub.status.idle": "2021-06-21T19:00:22.041867Z",
     "shell.execute_reply": "2021-06-21T19:00:22.040560Z",
     "shell.execute_reply.started": "2021-06-21T19:00:16.345840Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (4.59.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Preprocessing the Text \n",
    "\n",
    "It is now time to preprocess the text and understand the output. We are going to be using the above functions and also estimate the time it takes to complete the entire preprocessing respectively. Taking into account the different preprocessing text values, we are going to be appending those values and understanding the output respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:22.044057Z",
     "iopub.status.busy": "2021-06-21T19:00:22.043666Z",
     "iopub.status.idle": "2021-06-21T19:00:22.845737Z",
     "shell.execute_reply": "2021-06-21T19:00:22.844953Z",
     "shell.execute_reply.started": "2021-06-21T19:00:22.044012Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 19236/19236 [00:00<00:00, 24420.95it/s]\n"
     ]
    }
   ],
   "source": [
    "# Combining all the above stundents \n",
    "from tqdm import tqdm\n",
    "preprocessed_text = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentence in tqdm(X_train['text'].values):\n",
    "    sent = decontracted(sentence)\n",
    "    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)\n",
    "    # https://gist.github.com/sebleier/554280\n",
    "    sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)\n",
    "    preprocessed_text.append(sent.lower().strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:22.848792Z",
     "iopub.status.busy": "2021-06-21T19:00:22.848506Z",
     "iopub.status.idle": "2021-06-21T19:00:22.856305Z",
     "shell.execute_reply": "2021-06-21T19:00:22.855099Z",
     "shell.execute_reply.started": "2021-06-21T19:00:22.848762Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['24 hours since dog put sleep rip old friend',\n",
       " 'not feeling comfortable today',\n",
       " 'hurray twin girls born beautiful may day',\n",
       " 'bah h8 waking',\n",
       " 'textmate crashed first time 3 months not bad actually textmate']"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_text[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:22.858481Z",
     "iopub.status.busy": "2021-06-21T19:00:22.857979Z",
     "iopub.status.idle": "2021-06-21T19:00:28.336823Z",
     "shell.execute_reply": "2021-06-21T19:00:28.335923Z",
     "shell.execute_reply.started": "2021-06-21T19:00:22.858439Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in range(len(X_train)):\n",
    "    X_train['text'].iloc[i] = preprocessed_text[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:28.338478Z",
     "iopub.status.busy": "2021-06-21T19:00:28.338110Z",
     "iopub.status.idle": "2021-06-21T19:00:28.350494Z",
     "shell.execute_reply": "2021-06-21T19:00:28.349525Z",
     "shell.execute_reply.started": "2021-06-21T19:00:28.338439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>text_words</th>\n",
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       "      <th>19832</th>\n",
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       "      <td>78</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>10340</th>\n",
       "      <td>not feeling comfortable today</td>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11044</th>\n",
       "      <td>hurray twin girls born beautiful may day</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>14088</th>\n",
       "      <td>bah h8 waking</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>20333</th>\n",
       "      <td>textmate crashed first time 3 months not bad a...</td>\n",
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       "      <td>14</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  text_length  \\\n",
       "19832        24 hours since dog put sleep rip old friend           78   \n",
       "10340                      not feeling comfortable today           41   \n",
       "11044           hurray twin girls born beautiful may day           48   \n",
       "14088                                      bah h8 waking           19   \n",
       "20333  textmate crashed first time 3 months not bad a...           80   \n",
       "\n",
       "       text_words  \n",
       "19832          18  \n",
       "10340           6  \n",
       "11044           9  \n",
       "14088           5  \n",
       "20333          14  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:28.353111Z",
     "iopub.status.busy": "2021-06-21T19:00:28.352217Z",
     "iopub.status.idle": "2021-06-21T19:00:28.711913Z",
     "shell.execute_reply": "2021-06-21T19:00:28.710878Z",
     "shell.execute_reply.started": "2021-06-21T19:00:28.353070Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8244/8244 [00:00<00:00, 23929.39it/s]\n"
     ]
    }
   ],
   "source": [
    "# Combining all the above stundents \n",
    "from tqdm import tqdm\n",
    "preprocessed_text = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentence in tqdm(X_cv['text'].values):\n",
    "    sent = decontracted(sentence)\n",
    "    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)\n",
    "    # https://gist.github.com/sebleier/554280\n",
    "    sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)\n",
    "    preprocessed_text.append(sent.lower().strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:28.714149Z",
     "iopub.status.busy": "2021-06-21T19:00:28.713553Z",
     "iopub.status.idle": "2021-06-21T19:00:28.720360Z",
     "shell.execute_reply": "2021-06-21T19:00:28.719322Z",
     "shell.execute_reply.started": "2021-06-21T19:00:28.714106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mom happy present yayy',\n",
       " 'no surprise probably know',\n",
       " 'nope difference 5 omr c class anyone notice flight number wy flight lhr wy 911',\n",
       " 'smells badly garlic',\n",
       " 'friday whole different meaning work saturday sunday']"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_text[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:28.722334Z",
     "iopub.status.busy": "2021-06-21T19:00:28.721943Z",
     "iopub.status.idle": "2021-06-21T19:00:30.858156Z",
     "shell.execute_reply": "2021-06-21T19:00:30.857278Z",
     "shell.execute_reply.started": "2021-06-21T19:00:28.722293Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in range(len(X_cv)):\n",
    "    X_cv['text'].iloc[i] = preprocessed_text[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:30.859939Z",
     "iopub.status.busy": "2021-06-21T19:00:30.859581Z",
     "iopub.status.idle": "2021-06-21T19:00:30.871010Z",
     "shell.execute_reply": "2021-06-21T19:00:30.869987Z",
     "shell.execute_reply.started": "2021-06-21T19:00:30.859901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>text_words</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7214</th>\n",
       "      <td>mom happy present yayy</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19840</th>\n",
       "      <td>no surprise probably know</td>\n",
       "      <td>67</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>19409</th>\n",
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       "      <td>19</td>\n",
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       "      <th>6600</th>\n",
       "      <td>smells badly garlic</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7807</th>\n",
       "      <td>friday whole different meaning work saturday s...</td>\n",
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       "      <td>14</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  text_length  \\\n",
       "7214                              mom happy present yayy           38   \n",
       "19840                          no surprise probably know           67   \n",
       "19409  nope difference 5 omr c class anyone notice fl...          102   \n",
       "6600                                 smells badly garlic           25   \n",
       "7807   friday whole different meaning work saturday s...           78   \n",
       "\n",
       "       text_words  \n",
       "7214            8  \n",
       "19840          11  \n",
       "19409          19  \n",
       "6600            5  \n",
       "7807           14  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:30.872857Z",
     "iopub.status.busy": "2021-06-21T19:00:30.872424Z",
     "iopub.status.idle": "2021-06-21T19:00:30.973184Z",
     "shell.execute_reply": "2021-06-21T19:00:30.972442Z",
     "shell.execute_reply.started": "2021-06-21T19:00:30.872818Z"
    }
   },
   "outputs": [],
   "source": [
    "binarizer = LabelBinarizer()\n",
    "y_train_converted = binarizer.fit_transform(y_train)\n",
    "binarizer = LabelBinarizer()\n",
    "y_cv_converted = binarizer.fit_transform(y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:30.974762Z",
     "iopub.status.busy": "2021-06-21T19:00:30.974418Z",
     "iopub.status.idle": "2021-06-21T19:00:30.980015Z",
     "shell.execute_reply": "2021-06-21T19:00:30.979208Z",
     "shell.execute_reply.started": "2021-06-21T19:00:30.974726Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       ...,\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 0]])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cv_converted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Tfidf Vectorizer\n",
    "\n",
    "With the help of Tfidf Vectorizer, it is easy to convert the text into the form of vector based on the word frequency and the inverse document frequency and get those values which could be fed to the machine learning models for prediction respectively. Having a look at those values, we are going to be taking them and predicting using the machine learning approach. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:30.981955Z",
     "iopub.status.busy": "2021-06-21T19:00:30.981388Z",
     "iopub.status.idle": "2021-06-21T19:00:31.323447Z",
     "shell.execute_reply": "2021-06-21T19:00:31.322543Z",
     "shell.execute_reply.started": "2021-06-21T19:00:30.981910Z"
    }
   },
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()\n",
    "X_train_text = vectorizer.fit_transform(X_train['text'])\n",
    "X_cv_text = vectorizer.transform(X_cv['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.325177Z",
     "iopub.status.busy": "2021-06-21T19:00:31.324816Z",
     "iopub.status.idle": "2021-06-21T19:00:31.331325Z",
     "shell.execute_reply": "2021-06-21T19:00:31.330311Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.325136Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19236, 20619)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_text.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.333094Z",
     "iopub.status.busy": "2021-06-21T19:00:31.332709Z",
     "iopub.status.idle": "2021-06-21T19:00:31.344371Z",
     "shell.execute_reply": "2021-06-21T19:00:31.343123Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.333057Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5x20619 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 31 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_text[0: 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Neural Network Model for Prediction \n",
    "\n",
    "We are going to be defining the neural network model and predicting the chances of a person suffering from a heart disease. Taking a look at the data, we see that there are different activation units that we are going to be working with and then, we are going to use the categorical cross entropy as this is a multiclass classification problem. There can be more metrics that we might take but let us deal with accuracy for now and use the adam optimizer respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.346247Z",
     "iopub.status.busy": "2021-06-21T19:00:31.345878Z",
     "iopub.status.idle": "2021-06-21T19:00:31.400522Z",
     "shell.execute_reply": "2021-06-21T19:00:31.399494Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.346208Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, activation = 'relu', input_shape = (20619,)))\n",
    "model.add(Dense(50, activation = 'relu'))\n",
    "model.add(Dense(25, activation = 'relu'))\n",
    "model.add(Dense(10, activation = 'relu'))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "\n",
    "model.compile(loss = 'categorical_crossentropy', metrics = ['accuracy'], optimizer = 'adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
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       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]], dtype=float32)"
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     },
     "execution_count": 175,
     "metadata": {},
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    }
   ],
   "source": [
    "to_categorical(np.arange(1, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "execution": {
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       "19832    negative\n",
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       "11044    positive\n",
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       "Name: sentiment, Length: 19236, dtype: object"
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   ],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now going to be converting the output values in a series format so that we can give these values to the neural network models that we would be working respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
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     "shell.execute_reply.started": "2021-06-21T19:00:31.430794Z"
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   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "y_train_converted = encoder.fit_transform(y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
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     "execution_count": 178,
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  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-06-21T19:00:31.457536Z"
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   "outputs": [],
   "source": [
    "y_train_final = to_categorical(y_train_converted)"
   ]
  },
  {
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   "execution_count": 180,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text_length</th>\n",
       "      <th>text_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7214</th>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19840</th>\n",
       "      <td>67</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19409</th>\n",
       "      <td>102</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6600</th>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7807</th>\n",
       "      <td>78</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       text_length  text_words\n",
       "7214            38           8\n",
       "19840           67          11\n",
       "19409          102          19\n",
       "6600            25           5\n",
       "7807            78          14"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cv_dropped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to be converting the values from 0 to 1 respectively. We would have to be converting those values so that it becomes easy to perform machine learning analysis and this ensures that there is no gradient shifting and having different range of weights when performing the machine learning analysis respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.575611Z",
     "iopub.status.busy": "2021-06-21T19:00:31.575202Z",
     "iopub.status.idle": "2021-06-21T19:00:31.588029Z",
     "shell.execute_reply": "2021-06-21T19:00:31.587072Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.575572Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_train_final = scaler.fit_transform(X_train_dropped)\n",
    "X_cv_final = scaler.transform(X_cv_dropped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.590783Z",
     "iopub.status.busy": "2021-06-21T19:00:31.590100Z",
     "iopub.status.idle": "2021-06-21T19:00:31.597607Z",
     "shell.execute_reply": "2021-06-21T19:00:31.596444Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.590717Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.54347826, 0.5483871 ],\n",
       "       [0.27536232, 0.16129032],\n",
       "       [0.32608696, 0.25806452],\n",
       "       [0.11594203, 0.12903226],\n",
       "       [0.55797101, 0.41935484]])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.599885Z",
     "iopub.status.busy": "2021-06-21T19:00:31.599453Z",
     "iopub.status.idle": "2021-06-21T19:00:31.606622Z",
     "shell.execute_reply": "2021-06-21T19:00:31.605448Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.599845Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.25362319, 0.22580645],\n",
       "       [0.46376812, 0.32258065],\n",
       "       [0.7173913 , 0.58064516],\n",
       "       [0.15942029, 0.12903226],\n",
       "       [0.54347826, 0.41935484]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cv_final[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.608955Z",
     "iopub.status.busy": "2021-06-21T19:00:31.608218Z",
     "iopub.status.idle": "2021-06-21T19:00:31.623577Z",
     "shell.execute_reply": "2021-06-21T19:00:31.622818Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.608913Z"
    }
   },
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "y_train_encoded = encoder.fit_transform(y_train)\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "y_cv_encoded = encoder.fit_transform(y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.625681Z",
     "iopub.status.busy": "2021-06-21T19:00:31.625151Z",
     "iopub.status.idle": "2021-06-21T19:00:31.634169Z",
     "shell.execute_reply": "2021-06-21T19:00:31.633089Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.625641Z"
    }
   },
   "outputs": [],
   "source": [
    "y_train_final = to_categorical(y_train_encoded)\n",
    "y_cv_final = to_categorical(y_cv_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.637748Z",
     "iopub.status.busy": "2021-06-21T19:00:31.637477Z",
     "iopub.status.idle": "2021-06-21T19:00:31.645756Z",
     "shell.execute_reply": "2021-06-21T19:00:31.644476Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.637718Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_final[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.647957Z",
     "iopub.status.busy": "2021-06-21T19:00:31.647521Z",
     "iopub.status.idle": "2021-06-21T19:00:31.654765Z",
     "shell.execute_reply": "2021-06-21T19:00:31.653718Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.647919Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cv_final[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.657243Z",
     "iopub.status.busy": "2021-06-21T19:00:31.656463Z",
     "iopub.status.idle": "2021-06-21T19:00:31.664998Z",
     "shell.execute_reply": "2021-06-21T19:00:31.663747Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.657203Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.54347826, 0.5483871 ],\n",
       "       [0.27536232, 0.16129032],\n",
       "       [0.32608696, 0.25806452],\n",
       "       [0.11594203, 0.12903226],\n",
       "       [0.55797101, 0.41935484]])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.667443Z",
     "iopub.status.busy": "2021-06-21T19:00:31.666938Z",
     "iopub.status.idle": "2021-06-21T19:00:31.680710Z",
     "shell.execute_reply": "2021-06-21T19:00:31.679633Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.667401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>text_length</th>\n",
       "      <th>text_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19832</th>\n",
       "      <td>24 hours since dog put sleep rip old friend</td>\n",
       "      <td>78</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10340</th>\n",
       "      <td>not feeling comfortable today</td>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11044</th>\n",
       "      <td>hurray twin girls born beautiful may day</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14088</th>\n",
       "      <td>bah h8 waking</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20333</th>\n",
       "      <td>textmate crashed first time 3 months not bad a...</td>\n",
       "      <td>80</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  text_length  \\\n",
       "19832        24 hours since dog put sleep rip old friend           78   \n",
       "10340                      not feeling comfortable today           41   \n",
       "11044           hurray twin girls born beautiful may day           48   \n",
       "14088                                      bah h8 waking           19   \n",
       "20333  textmate crashed first time 3 months not bad a...           80   \n",
       "\n",
       "       text_words  \n",
       "19832          18  \n",
       "10340           6  \n",
       "11044           9  \n",
       "14088           5  \n",
       "20333          14  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Count Vectorizer\n",
    "\n",
    "Now we are going to be using the bag of words to understand the text and get a good knowledge about it. Since the data that is given to the machine learning models need to be in the form of vectors, it would be good to convert the text values into different vectors so that it becomes easy for the machine learning models to perform the computations respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:31.682603Z",
     "iopub.status.busy": "2021-06-21T19:00:31.682133Z",
     "iopub.status.idle": "2021-06-21T19:00:32.032140Z",
     "shell.execute_reply": "2021-06-21T19:00:32.031172Z",
     "shell.execute_reply.started": "2021-06-21T19:00:31.682561Z"
    }
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "X_train_vectorized = vectorizer.fit_transform(X_train['text'])\n",
    "X_cv_vectorized = vectorizer.transform(X_cv['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:32.034284Z",
     "iopub.status.busy": "2021-06-21T19:00:32.033895Z",
     "iopub.status.idle": "2021-06-21T19:00:32.040415Z",
     "shell.execute_reply": "2021-06-21T19:00:32.039394Z",
     "shell.execute_reply.started": "2021-06-21T19:00:32.034243Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<19236x20619 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 135844 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_vectorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:32.042682Z",
     "iopub.status.busy": "2021-06-21T19:00:32.042202Z",
     "iopub.status.idle": "2021-06-21T19:00:32.053268Z",
     "shell.execute_reply": "2021-06-21T19:00:32.052038Z",
     "shell.execute_reply.started": "2021-06-21T19:00:32.042639Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.54347826, 0.5483871 ],\n",
       "       [0.27536232, 0.16129032],\n",
       "       [0.32608696, 0.25806452],\n",
       "       [0.11594203, 0.12903226],\n",
       "       [0.55797101, 0.41935484]])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final[0: 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the values that we have are not in the form of arrays and in the form of sparse matrices, it would be a good idea to convert the values into the form of arrays so that it becomes easy for the machine learning models to make the predictions and ensure that they are getting the best predictions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:32.055541Z",
     "iopub.status.busy": "2021-06-21T19:00:32.055030Z",
     "iopub.status.idle": "2021-06-21T19:00:32.430003Z",
     "shell.execute_reply": "2021-06-21T19:00:32.429079Z",
     "shell.execute_reply.started": "2021-06-21T19:00:32.055499Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train_bow_toarray = X_train_vectorized.toarray()\n",
    "X_cv_bow_toarray = X_cv_vectorized.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have formed the arrays that we would be using and now it is time to understand the matrices and concatenate them so that we are going to perform the machine learning analysis respectively. We are now going to be using the deep neural networks and this would ensure that we are getting the best results respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:32.431912Z",
     "iopub.status.busy": "2021-06-21T19:00:32.431497Z",
     "iopub.status.idle": "2021-06-21T19:00:34.413882Z",
     "shell.execute_reply": "2021-06-21T19:00:34.412914Z",
     "shell.execute_reply.started": "2021-06-21T19:00:32.431826Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train_new = np.concatenate((X_train_bow_toarray, X_train_final), axis = 1)\n",
    "X_cv_new = np.concatenate((X_cv_bow_toarray, X_cv_final), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. Neutral Network Model \n",
    "\n",
    "We would now be using the deep neural networks that we are going to be learning and ensure that we are getting the best predictions respectively. We would start with 100 neutrons from the first layer and followed by 25 neutrons in the second layer and 10 units in the third layer followed by 3 final layers which we are going to be using the softmax classifier for predictions respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:34.415723Z",
     "iopub.status.busy": "2021-06-21T19:00:34.415324Z",
     "iopub.status.idle": "2021-06-21T19:00:34.440214Z",
     "shell.execute_reply": "2021-06-21T19:00:34.439441Z",
     "shell.execute_reply.started": "2021-06-21T19:00:34.415680Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, activation = 'relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(25, activation = 'relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation = 'relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(3, activation = 'softmax'))\n",
    "model.compile(loss = 'categorical_crossentropy', metrics = ['accuracy'], optimizer = 'adam')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would be running the deep neutral network model for 10 epochs to ensure that we are going to be getting the best results in the test set respectively. We are also going to give the cross validation data and see how our model would be performing with the cross validation data, taking into account different parameters such as accuracy and cross validation loss respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:00:34.441944Z",
     "iopub.status.busy": "2021-06-21T19:00:34.441512Z",
     "iopub.status.idle": "2021-06-21T19:01:05.247187Z",
     "shell.execute_reply": "2021-06-21T19:01:05.246290Z",
     "shell.execute_reply.started": "2021-06-21T19:00:34.441904Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "602/602 [==============================] - 4s 6ms/step - loss: 1.0098 - accuracy: 0.4828 - val_loss: 0.7593 - val_accuracy: 0.6862\n",
      "Epoch 2/10\n",
      "602/602 [==============================] - 3s 4ms/step - loss: 0.6913 - accuracy: 0.7126 - val_loss: 0.7227 - val_accuracy: 0.6972\n",
      "Epoch 3/10\n",
      "602/602 [==============================] - 3s 5ms/step - loss: 0.4995 - accuracy: 0.8079 - val_loss: 0.7762 - val_accuracy: 0.6896\n",
      "Epoch 4/10\n",
      "602/602 [==============================] - 3s 4ms/step - loss: 0.3655 - accuracy: 0.8646 - val_loss: 0.8712 - val_accuracy: 0.6855\n",
      "Epoch 5/10\n",
      "602/602 [==============================] - 3s 5ms/step - loss: 0.2798 - accuracy: 0.8988 - val_loss: 1.0168 - val_accuracy: 0.6788\n",
      "Epoch 6/10\n",
      "602/602 [==============================] - 3s 4ms/step - loss: 0.2369 - accuracy: 0.9169 - val_loss: 1.1245 - val_accuracy: 0.6769\n",
      "Epoch 7/10\n",
      "602/602 [==============================] - 3s 4ms/step - loss: 0.1777 - accuracy: 0.9383 - val_loss: 1.2873 - val_accuracy: 0.6753\n",
      "Epoch 8/10\n",
      "602/602 [==============================] - 3s 4ms/step - loss: 0.1470 - accuracy: 0.9479 - val_loss: 1.3884 - val_accuracy: 0.6758\n",
      "Epoch 9/10\n",
      "602/602 [==============================] - 3s 5ms/step - loss: 0.1332 - accuracy: 0.9543 - val_loss: 1.5644 - val_accuracy: 0.6722\n",
      "Epoch 10/10\n",
      "602/602 [==============================] - 3s 5ms/step - loss: 0.1151 - accuracy: 0.9610 - val_loss: 1.6192 - val_accuracy: 0.6718\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fe9a84b5cd0>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_new, y_train_final, epochs = 10, validation_data = (X_cv_new, y_cv_final))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13. Plots of the Results \n",
    "\n",
    "We would now be looking at the deep neural network plots and see how the values of accuracy and loss change with respect to the number of epochs that we are running. We see that as the number of epochs increase, there seems to be overfitting where the cross validation loss tends to go higher though there is a decrease in the training loss respectively. This gives us a clear indication that the model is overfitting after certain number of epochs are met. Therefore, we would be taking the right number of epochs when we are performing the machine learning analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-21T19:01:05.250052Z",
     "iopub.status.busy": "2021-06-21T19:01:05.249732Z",
     "iopub.status.idle": "2021-06-21T19:01:05.583036Z",
     "shell.execute_reply": "2021-06-21T19:01:05.582220Z",
     "shell.execute_reply.started": "2021-06-21T19:01:05.250024Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = model.history.history['accuracy']\n",
    "val_accuracy = model.history.history['val_accuracy']\n",
    "loss = model.history.history['loss']\n",
    "val_loss = model.history.history['val_loss']\n",
    "epochs = np.arange(1, 11)\n",
    "fig, ax = plt.subplots(1, 2, figsize = (20, 5))\n",
    "\n",
    "sns.lineplot(x = epochs, y = accuracy, ax = ax[0])\n",
    "sns.lineplot(x = epochs, y = val_accuracy, ax = ax[0])\n",
    "ax[0].set_title('Accuracy Vs Epochs')\n",
    "sns.lineplot(x = epochs, y = loss, ax = ax[1])\n",
    "sns.lineplot(x = epochs, y = val_loss, ax = ax[1])\n",
    "ax[1].set_title('Loss Vs Epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14. Conclusions \n",
    "\n",
    "1. It would be a good idea to use some tools such as wordcloud when we are doing Natural Language Processing (NLP) to ensure that we are getting the best results for predictions respectively. We would be able to understand the frequently occurring words from the less frequently occurring words by the size of the words that are plotted in the wordcloud respectively. \n",
    "2. Steps should be taken to ensure that the model does not overfit or underfit. This ensures that the best predictions are being generated and therefore, we are going to get the best outputs respectively. \n",
    "3. Standarizing the text and ensuring that the values lie between 0 and 1 would be good as this would allow the machine learning models to generate weights that are quite small rather than having different weight range values. "
   ]
  }
 ],
 "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": "3.7.10"
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 },
 "nbformat": 4,
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}