Text Mining(Elon-Musk Tweets).ipynb

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  {
   "cell_type": "markdown",
   "id": "67527c17",
   "metadata": {},
   "source": [
    "# Text Mining(Elon-Musk Tweets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9cf2922",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:39:29.187207Z",
     "start_time": "2021-08-30T17:39:29.178213Z"
    }
   },
   "source": [
    "Perform sentimental analysis on the Elon-musk tweets (Exlon-musk.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e8787cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:42:17.514593Z",
     "start_time": "2021-08-30T17:41:54.418756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: textblob in c:\\users\\raman\\anaconda3\\lib\\site-packages (0.15.3)\n",
      "Requirement already satisfied: nltk>=3.1 in c:\\users\\raman\\anaconda3\\lib\\site-packages (from textblob) (3.6.1)\n",
      "Requirement already satisfied: joblib in c:\\users\\raman\\anaconda3\\lib\\site-packages (from nltk>=3.1->textblob) (1.0.1)\n",
      "Requirement already satisfied: click in c:\\users\\raman\\anaconda3\\lib\\site-packages (from nltk>=3.1->textblob) (7.1.2)\n",
      "Requirement already satisfied: regex in c:\\users\\raman\\anaconda3\\lib\\site-packages (from nltk>=3.1->textblob) (2021.4.4)\n",
      "Requirement already satisfied: tqdm in c:\\users\\raman\\anaconda3\\lib\\site-packages (from nltk>=3.1->textblob) (4.59.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install textblob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f03a8718",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:42:40.875946Z",
     "start_time": "2021-08-30T17:42:17.550550Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\raman\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Importing all required library\n",
    "import re\n",
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from textblob import TextBlob\n",
    "import matplotlib.pyplot as plt\n",
    "import re  \n",
    "  \n",
    "# Natural Language Tool Kit \n",
    "import nltk  \n",
    "  \n",
    "nltk.download('stopwords') \n",
    "  \n",
    "# to remove stopword \n",
    "from nltk.corpus import stopwords \n",
    "  \n",
    "# for Stemming propose  \n",
    "from nltk.stem.porter import PorterStemmer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "acdb1add",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:42:41.050379Z",
     "start_time": "2021-08-30T17:42:40.928917Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Text</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>@kunalb11 Im an alien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>@ID_AA_Carmack Ray tracing on Cyberpunk with H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>@joerogan @Spotify Great interview!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>@gtera27 Doge is underestimated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>@teslacn Congratulations Tesla China for amazi...</td>\n",
       "    </tr>\n",
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       "</table>\n",
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       "   Unnamed: 0                                               Text\n",
       "0           1                             @kunalb11 Im an alien\n",
       "1           2  @ID_AA_Carmack Ray tracing on Cyberpunk with H...\n",
       "2           3                @joerogan @Spotify Great interview!\n",
       "3           4                    @gtera27 Doge is underestimated\n",
       "4           5  @teslacn Congratulations Tesla China for amazi..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Elon_musk.csv', encoding = \"latin-1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "450a4a54",
   "metadata": {
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     "start_time": "2021-08-30T17:42:41.102881Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                               @kunalb11 im an alien\n",
       "1    @id_aa_carmack ray tracing on cyberpunk with h...\n",
       "2                  @joerogan @spotify great interview!\n",
       "3                      @gtera27 doge is underestimated\n",
       "4    @teslacn congratulations tesla china for amazi...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## PREPROCESSING\n",
    "## Lower Case\n",
    "df['Text'] = df['Text'].apply(lambda x: \" \".join(x.lower() for x in x.split()))\n",
    "df['Text'].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c574500",
   "metadata": {
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     "end_time": "2021-08-30T17:42:41.587171Z",
     "start_time": "2021-08-30T17:42:41.471369Z"
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-42401c5871d6>:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['Text'] = df['Text'].str.replace('[^\\w\\s]','')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0                                 kunalb11 im an alien\n",
       "1    id_aa_carmack ray tracing on cyberpunk with hd...\n",
       "2                     joerogan spotify great interview\n",
       "3                       gtera27 doge is underestimated\n",
       "4    teslacn congratulations tesla china for amazin...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Removing Puncution\n",
    "df['Text'] = df['Text'].str.replace('[^\\w\\s]','')\n",
    "df['Text'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ad6c79e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:42:41.860806Z",
     "start_time": "2021-08-30T17:42:41.624283Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                    kunalb11 im alien\n",
       "1    id_aa_carmack ray tracing cyberpunk hdr nextle...\n",
       "2                     joerogan spotify great interview\n",
       "3                          gtera27 doge underestimated\n",
       "4    teslacn congratulations tesla china amazing ex...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Removing StopWords\n",
    "stop = stopwords.words('english')\n",
    "df['Text'] = df['Text'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop))\n",
    "df['Text'].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54d98553",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:42:48.705823Z",
     "start_time": "2021-08-30T17:42:47.053147Z"
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       "   Unnamed: 0                                               Text  \\\n",
       "0           1                                  kunalb11 im alien   \n",
       "1           2  id_aa_carmack ray tracing cyberpunk hdr nextle...   \n",
       "2           3                   joerogan spotify great interview   \n",
       "3           4                        gtera27 doge underestimated   \n",
       "4           5  teslacn congratulations tesla china amazing ex...   \n",
       "5           6               happy new year ox httpstco9wfkmyu2oj   \n",
       "6           7  frodo underdoge thought would fail httpstcozgx...   \n",
       "7           8           owensparks_ flcnhvy anonyx10 haha thanks   \n",
       "8           9  flcnhvy anonyx10 indeed tweets definitely repr...   \n",
       "9          10                        entertaining outcome likely   \n",
       "\n",
       "   Subjectivity  Polarity  \n",
       "0      0.750000 -0.250000  \n",
       "1      0.000000  0.000000  \n",
       "2      0.750000  0.800000  \n",
       "3      0.000000  0.000000  \n",
       "4      0.322222  0.200000  \n",
       "5      0.727273  0.468182  \n",
       "6      0.300000 -0.500000  \n",
       "7      0.250000  0.200000  \n",
       "8      0.500000  0.000000  \n",
       "9      0.850000  0.250000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's calculate subjectivity and Polarity\n",
    "# function for subjectivity\n",
    "def calc_subj(Text):\n",
    "    return TextBlob(Text).sentiment.subjectivity\n",
    " \n",
    "# function for Polarity\n",
    "def calc_pola(Text):\n",
    "    return TextBlob(Text).sentiment.polarity\n",
    " \n",
    "df['Subjectivity'] = df.Text.apply(calc_subj)\n",
    "df['Polarity'] = df.Text.apply(calc_pola)\n",
    "1\n",
    "2\n",
    "# let's have quick look to our dataset\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cdb22228",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:42:56.990652Z",
     "start_time": "2021-08-30T17:42:56.959103Z"
    }
   },
   "outputs": [
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       "      <th>Text</th>\n",
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       "      <th>Polarity</th>\n",
       "      <th>Sentiment</th>\n",
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       "      <td>kunalb11 im alien</td>\n",
       "      <td>0.750000</td>\n",
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       "      <td>Negative</td>\n",
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       "      <td>joerogan spotify great interview</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.80</td>\n",
       "      <td>Positive</td>\n",
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       "      <td>0.20</td>\n",
       "      <td>Positive</td>\n",
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      "text/plain": [
       "   Unnamed: 0                                               Text  \\\n",
       "0           1                                  kunalb11 im alien   \n",
       "1           2  id_aa_carmack ray tracing cyberpunk hdr nextle...   \n",
       "2           3                   joerogan spotify great interview   \n",
       "3           4                        gtera27 doge underestimated   \n",
       "4           5  teslacn congratulations tesla china amazing ex...   \n",
       "\n",
       "   Subjectivity  Polarity Sentiment  \n",
       "0      0.750000     -0.25  Negative  \n",
       "1      0.000000      0.00   Netural  \n",
       "2      0.750000      0.80  Positive  \n",
       "3      0.000000      0.00   Netural  \n",
       "4      0.322222      0.20  Positive  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now let's classify these tweets based on their sentiment(polarity)\n",
    "def sentiment(polarity):\n",
    "    result = ''\n",
    "    if polarity > 0:\n",
    "        result = 'Positive'\n",
    "    elif polarity == 0:\n",
    "        result = 'Netural'\n",
    "    else:\n",
    "        result = 'Negative'\n",
    "    return result\n",
    " \n",
    "df['Sentiment'] = df.Polarity.apply(sentiment)\n",
    "1\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3c4177f0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:43:13.535947Z",
     "start_time": "2021-08-30T17:43:12.304980Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see how many ratio of sentiment\n",
    "df.Sentiment.value_counts().plot(kind='bar', color='red')\n",
    "plt.title('Sentiment Classification')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d407bed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:43:20.920002Z",
     "start_time": "2021-08-30T17:43:20.448657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Subjectivity')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.Polarity, df.Subjectivity, color='red')\n",
    "plt.title('Sentiment Analysis')\n",
    "plt.xlabel('Polarity')\n",
    "plt.ylabel('Subjectivity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5249a93a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T17:43:29.515079Z",
     "start_time": "2021-08-30T17:43:28.813146Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Netural</th>\n",
       "      <td>48.024012</td>\n",
       "      <td>960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Positive</th>\n",
       "      <td>42.021011</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Negative</th>\n",
       "      <td>9.954977</td>\n",
       "      <td>199</td>\n",
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      "text/plain": [
       "          Sentiment  Total\n",
       "Netural   48.024012    960\n",
       "Positive  42.021011    840\n",
       "Negative   9.954977    199"
      ]
     },
     "execution_count": 12,
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   ],
   "source": [
    "# let's see the percentage of different sentiment's class\n",
    " \n",
    "# Creat\n",
    "Df_sentiment = pd.DataFrame(df.Sentiment.value_counts(normalize=True)*100)\n",
    " \n",
    "# calculating percentage\n",
    "Df_sentiment['Total'] = df.Sentiment.value_counts()\n",
    "\n",
    "Df_sentiment"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "d37a9ed5",
   "metadata": {},
   "outputs": [],
   "source": []
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