text_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4d18d01d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "from gensim.models import Word2Vec,KeyedVectors\n",
    "import os\n",
    "import wget\n",
    "import gzip\n",
    "import shutil\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "194f017a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'all-data.csv',encoding = \"ISO-8859-1\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f75de7f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>According to Gran , the company has no plans t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neutral</td>\n",
       "      <td>Technopolis plans to develop in stages an area...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>negative</td>\n",
       "      <td>The international electronic industry company ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>positive</td>\n",
       "      <td>With the new production plant the company woul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>positive</td>\n",
       "      <td>According to the company 's updated strategy f...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0                                                  1\n",
       "0   neutral  According to Gran , the company has no plans t...\n",
       "1   neutral  Technopolis plans to develop in stages an area...\n",
       "2  negative  The international electronic industry company ...\n",
       "3  positive  With the new production plant the company woul...\n",
       "4  positive  According to the company 's updated strategy f..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7f23778",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=['label','texts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "131cbf4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>texts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>According to Gran , the company has no plans t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neutral</td>\n",
       "      <td>Technopolis plans to develop in stages an area...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>negative</td>\n",
       "      <td>The international electronic industry company ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>positive</td>\n",
       "      <td>With the new production plant the company woul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>positive</td>\n",
       "      <td>According to the company 's updated strategy f...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      label                                              texts\n",
       "0   neutral  According to Gran , the company has no plans t...\n",
       "1   neutral  Technopolis plans to develop in stages an area...\n",
       "2  negative  The international electronic industry company ...\n",
       "3  positive  With the new production plant the company woul...\n",
       "4  positive  According to the company 's updated strategy f..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ea696c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neutral     0.594098\n",
       "positive    0.281263\n",
       "negative    0.124639\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.value_counts()/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f919b7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words=stopwords.words(\"english\")\n",
    "def preprocess(df):\n",
    "#     function to remove stopwords,digts and punctions  \n",
    "    df['texts']=df['texts'].apply(lambda x:\" \".join(x.lower() for x in x.split()))\n",
    "    df['texts']=df['texts'].apply(lambda x:\" \".join(x for x in x.split() if x not in stop_words))\n",
    "    df['texts']=df['texts'].apply(lambda x:\" \".join(x for x in x.split() if not x.isdigit()))\n",
    "    df['texts']=df['texts'].str.replace('[^\\w\\s]','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8cfed05",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocess(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "912e835e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>texts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>according gran company plans move production r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>technopolis plans develop stages area less squ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>international electronic industry company elco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>new production plant company would increase ca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>according company updated strategy years baswa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>4841</th>\n",
       "      <td>2</td>\n",
       "      <td>london marketwatch share prices ended lower lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4842</th>\n",
       "      <td>0</td>\n",
       "      <td>rinkuskiai beer sales fell per cent million li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4843</th>\n",
       "      <td>2</td>\n",
       "      <td>operating profit fell eur mn eur mn including ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4844</th>\n",
       "      <td>2</td>\n",
       "      <td>net sales paper segment decreased eur mn secon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4845</th>\n",
       "      <td>2</td>\n",
       "      <td>sales finland decreased january sales outside ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4846 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      label                                              texts\n",
       "0         0  according gran company plans move production r...\n",
       "1         0  technopolis plans develop stages area less squ...\n",
       "2         2  international electronic industry company elco...\n",
       "3         1  new production plant company would increase ca...\n",
       "4         1  according company updated strategy years baswa...\n",
       "...     ...                                                ...\n",
       "4841      2  london marketwatch share prices ended lower lo...\n",
       "4842      0  rinkuskiai beer sales fell per cent million li...\n",
       "4843      2  operating profit fell eur mn eur mn including ...\n",
       "4844      2  net sales paper segment decreased eur mn secon...\n",
       "4845      2  sales finland decreased january sales outside ...\n",
       "\n",
       "[4846 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "99568888",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['label']=df.label.map({'neutral':0,'positive':1,\"negative\":2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "58d5f15d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>texts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>according gran company plans move production r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>technopolis plans develop stages area less squ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>international electronic industry company elco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>new production plant company would increase ca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>according company updated strategy years baswa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                              texts\n",
       "0      0  according gran company plans move production r...\n",
       "1      0  technopolis plans develop stages area less squ...\n",
       "2      2  international electronic industry company elco...\n",
       "3      1  new production plant company would increase ca...\n",
       "4      1  according company updated strategy years baswa..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5c28c260",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df['texts']\n",
    "y=df['label']\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=12,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da3b7364",
   "metadata": {},
   "outputs": [],
   "source": [
    "vec=CountVectorizer()\n",
    "x_train_vec=vec.fit_transform(x_train)\n",
    "x_test_vec=vec.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "079c126b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb=MultinomialNB()\n",
    "nb.fit(x_train_vec,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "37c41a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_y_pred=nb.predict(x_test_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c09728f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[711, 158,  73],\n",
       "       [114, 217,  51],\n",
       "       [ 16,  32,  82]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(nb_y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "588aeaf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6946354883081155"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(nb_y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "deb4a0cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc=SVC()\n",
    "svc.fit(x_train_vec,y_train)\n",
    "svc_y_pred=svc.predict(x_test_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7e20e73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[818, 236, 111],\n",
       "       [ 22, 157,  40],\n",
       "       [  1,  14,  55]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(svc_y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "223dd366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7083906464924347"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(svc_y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bb406faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_zip_path=r'D:\\jupyternotebooks\\Untitled Folder\\GoogleNews-vectors-negative300.bin.gz'\n",
    "file_vec_path=r'D:\\jupyternotebooks\\Untitled Folder\\GoogleNews-vectors-negative300.bin'\n",
    "with gzip.open(file_zip_path,'rb') as f_in:\n",
    "    with open(file_vec_path,'wb') as f_out:\n",
    "        shutil.copyfileobj(f_in,f_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5a4376d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model=KeyedVectors.load_word2vec_format(file_vec_path,binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "034651ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model_keys=w2v_model.vocab.keys()\n",
    "w2v_lower=[item.lower() for item in w2v_model_keys]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1af69a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_of_tokens(df):\n",
    "    list_of_list=[]\n",
    "    for index,row in df.iterrows():\n",
    "        row_text=row['texts']\n",
    "        list_of_list.append(row_text.split())\n",
    "    return list_of_list\n",
    "\n",
    "def embedding(list_of_list):\n",
    "    DIMENSION=300\n",
    "    zero_vector=np.zeros(DIMENSION)\n",
    "    feats=[]\n",
    "    for tokens in list_of_list:\n",
    "        feat_for_this=np.zeros(DIMENSION)\n",
    "        count_for_this=0+ 1e-5\n",
    "        for token in tokens:\n",
    "            if token in w2v_model:\n",
    "                feat_for_this+=w2v_model[token]\n",
    "                count_for_this+=1\n",
    "        if(count_for_this!=0):\n",
    "            feats.append(feat_for_this/count_for_this)\n",
    "        else:\n",
    "            feats.append(zero_vector)\n",
    "    return feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bf9ceed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_vectors=embedding(list_of_tokens(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "be866e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(train_vectors,y,random_state=12,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dc1f93b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc=SVC()\n",
    "svc.fit(x_train,y_train)\n",
    "svc_y_pred=svc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c8088154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.750343878954608"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(svc_y_pred,y_test)"
   ]
  }
 ],
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