Sentiment Analysis with Text Blob.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:44px;\"> <span style=\"color:#4B8BBE;\">TextBlob</span> for Python Developer </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from textblob import TextBlob\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "sn.set()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "txts = ['I love Machine learning', \n",
    "        'I do not like machine learning', \n",
    "         'I hate machine learning', \n",
    "        'Life is great', \n",
    "        'Thank you so much for everyhting',\n",
    "        'Such an interesting course'\n",
    "       ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(txts, columns=['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I love Machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I do not like machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I hate machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Life is great</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thank you so much for everyhting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Such an interesting course</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               text\n",
       "0           I love Machine learning\n",
       "1    I do not like machine learning\n",
       "2           I hate machine learning\n",
       "3                     Life is great\n",
       "4  Thank you so much for everyhting\n",
       "5        Such an interesting course"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n"
     ]
    }
   ],
   "source": [
    "blob = TextBlob('I so happy')\n",
    "print(blob.sentences[0].sentiment.polarity) # -1 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "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>polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I love Machine learning</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I do not like machine learning</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I hate machine learning</td>\n",
       "      <td>-0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Life is great</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thank you so much for everyhting</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Such an interesting course</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               text  polarity\n",
       "0           I love Machine learning      0.50\n",
       "1    I do not like machine learning      0.00\n",
       "2           I hate machine learning     -0.80\n",
       "3                     Life is great      0.80\n",
       "4  Thank you so much for everyhting      0.20\n",
       "5        Such an interesting course      0.25"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_polarity(txt):\n",
    "    blob = TextBlob(txt)\n",
    "    return blob.sentences[0].sentiment.polarity\n",
    "\n",
    "df['polarity'] = df['text'].apply(find_polarity)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_sentiment(txt):\n",
    "        if find_polarity(txt) > 0:\n",
    "            return 'Postitive'\n",
    "        elif find_polarity(txt) == 0:\n",
    "                return 'Neutral'\n",
    "        elif find_polarity(txt) < 0:\n",
    "            return 'Negative'\n",
    "        else:\n",
    "            pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sentiments'] = df['text'].apply(find_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "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>polarity</th>\n",
       "      <th>sentiments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I love Machine learning</td>\n",
       "      <td>0.50</td>\n",
       "      <td>Postitive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I do not like machine learning</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I hate machine learning</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Life is great</td>\n",
       "      <td>0.80</td>\n",
       "      <td>Postitive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thank you so much for everyhting</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Postitive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Such an interesting course</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Postitive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               text  polarity sentiments\n",
       "0           I love Machine learning      0.50  Postitive\n",
       "1    I do not like machine learning      0.00    Neutral\n",
       "2           I hate machine learning     -0.80   Negative\n",
       "3                     Life is great      0.80  Postitive\n",
       "4  Thank you so much for everyhting      0.20  Postitive\n",
       "5        Such an interesting course      0.25  Postitive"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "values = df['sentiments'].value_counts().values\n",
    "indexes = df['sentiments'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Sentiment Analysis using TextBlob')\n",
    "plt.xlabel('Sentiments')\n",
    "plt.ylabel('Number of Sentences')\n",
    "# plt.legend(indexes)\n",
    "plt.bar(indexes, values,color=['#7be382','#eedc82', '#cd5c5c'], alpha = 0.7)\n",
    "plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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