Sentiment analysis using Word2vec and Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"labeledTrainData.tsv\", header=0,\\\n",
    "                    delimiter=\"\\t\", quoting=3)\n",
    "\n",
    "test = pd.read_csv(\"testData.tsv\",header=0,\\\n",
    "                    delimiter=\"\\t\", quoting=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def review_word_list(review):\n",
    "    review_text=BeautifulSoup(review,'lxml').get_text()\n",
    "    review_text = re.sub(\"[^a-zA-Z]\",\" \",review_text)\n",
    "    words= review_text.lower().split()\n",
    "    words= [w for w in words if not w in set(stopwords.words('english'))]\n",
    "    return words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk.data\n",
    "tokenizer= nltk.data.load('tokenizers/punkt/english.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def review_sentence(review, tokenizer):\n",
    "    raw_sentences = tokenizer.tokenize(review.strip())\n",
    "    sentences=[]\n",
    "    for raw_sentence in raw_sentences:\n",
    "        if len(raw_sentence)>0:\n",
    "            a=review_word_list(raw_sentence)\n",
    "            sentences.append(a)\n",
    "    \n",
    "    return sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 out of  25000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'.'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 out of  25000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.happierabroad.com\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000 out of  25000\n",
      "3000 out of  25000\n",
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      "5000 out of  25000\n",
      "6000 out of  25000\n",
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     ]
    }
   ],
   "source": [
    "sentences=[]\n",
    "i=0\n",
    "for review in train['review']:\n",
    "    if i%1000==0:\n",
    "        print (i , \"out of \", len(train['review']))\n",
    "    \n",
    "    sentences+=review_sentence(review,tokenizer)\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1209: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model....\n"
     ]
    }
   ],
   "source": [
    "num_features = 300  # Word vector dimensionality\n",
    "min_word_count = 40 # Minimum word count\n",
    "num_workers = 4     # Number of parallel threads\n",
    "context = 10        # Context window size\n",
    "downsampling = 1e-3 # (0.001) Downsample setting for frequent words\n",
    "\n",
    "from gensim.models import word2vec\n",
    "print(\"Training model....\")\n",
    "model = word2vec.Word2Vec(sentences,\\\n",
    "                          workers=num_workers,\\\n",
    "                          size=num_features,\\\n",
    "                          min_count=min_word_count,\\\n",
    "                          window=context,\n",
    "                          sample=downsampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_name = \"300features_40minwords_10context\"\n",
    "model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\gensim\\matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'planet'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.doesnt_match(\"man woman dog child planet\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\gensim\\matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'berlin'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.doesnt_match(\"france england germany berlin\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\gensim\\matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('lady', 0.694207489490509),\n",
       " ('sexually', 0.691582441329956),\n",
       " ('lover', 0.6900498270988464),\n",
       " ('girl', 0.6889432668685913),\n",
       " ('loretta', 0.6845369338989258),\n",
       " ('giovanna', 0.6834275722503662),\n",
       " ('husband', 0.6786994934082031),\n",
       " ('man', 0.6738367676734924),\n",
       " ('boy', 0.6618785262107849),\n",
       " ('brides', 0.6567746996879578)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"woman\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\gensim\\matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('terrible', 0.8852403163909912),\n",
       " ('horrible', 0.863947868347168),\n",
       " ('atrocious', 0.7818443775177002),\n",
       " ('dreadful', 0.7764734029769897),\n",
       " ('crappy', 0.768346905708313),\n",
       " ('abysmal', 0.7656266689300537),\n",
       " ('sucks', 0.7594637870788574),\n",
       " ('pathetic', 0.7486364841461182),\n",
       " ('lousy', 0.7310115098953247),\n",
       " ('horrid', 0.7291818857192993)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"awful\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def features(words,num_features,model):\n",
    "    feature= np.zeros(num_features,dtype='float32')\n",
    "    nwords=0\n",
    "    \n",
    "    index2word_set = set(model.wv.index2word)\n",
    "    \n",
    "    for word in words:\n",
    "        if word in index2word_set:\n",
    "            nwords=nwords+1\n",
    "            \n",
    "            feature=np.add(feature,model[word])\n",
    "    \n",
    "    feature=np.divide(feature,nwords)\n",
    "    return feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg_features(reviews,num_features,model):\n",
    "    counter=0\n",
    "    avg_feature_vec=np.zeros((len(reviews),num_features),dtype='float32')\n",
    "    for review in reviews:\n",
    "        avg_feature_vec[counter]=features(review,num_features,model)\n",
    "        counter=counter+1\n",
    "    \n",
    "    return avg_feature_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "clean_train_reviews = []\n",
    "i=0\n",
    "for review in train['review']:\n",
    "    if i%1000==0:\n",
    "        print (i , \"out of \", len(train['review']))\n",
    "        \n",
    "    clean_train_reviews.append(review_word_list(review))\n",
    "    i=i+1\n",
    "\n",
    "train_data_vector = avg_features(clean_train_reviews,num_features,model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "clean_test_reviews = []\n",
    "i=0\n",
    "for review in test['review']:\n",
    "    if i%1000==0:\n",
    "        print (i , \"out of \", len(test['review']))\n",
    "        \n",
    "    clean_test_reviews.append(review_word_list(review))\n",
    "    i=i+1\n",
    "test_data_vector = avg_features(clean_test_reviews,num_features,model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting Logistic Regression to training data....\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.85144"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()   \n",
    "print(\"Fitting Logistic Regression to training data....\")    \n",
    "model.fit(train_data_vector, train[\"sentiment\"])\n",
    "pred=model.predict(train_data_vector)\n",
    "model.score(train_data_vector,train[\"sentiment\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10529  1971]\n",
      " [ 1743 10757]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(train[\"sentiment\"],pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.86      0.84      0.85     12500\n",
      "          1       0.85      0.86      0.85     12500\n",
      "\n",
      "avg / total       0.85      0.85      0.85     25000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(train[\"sentiment\"],pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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