rating_prediction1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named textblob",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-de6874fcb31b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;31m# sys.path.append('C:\\Python27\\Lib\\site-packages')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtextblob\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTextBlob\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m: No module named textblob"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import urllib\n",
    "import scipy.optimize\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import collections\n",
    "# from collections import defaultdict\n",
    "import gzip\n",
    "import string\n",
    "import re\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn import linear_model\n",
    "import nltk\n",
    "from nltk.tokenize import treebank\n",
    "from nltk.corpus import opinion_lexicon\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import sys\n",
    "# sys.path.append('C:\\Python27\\Lib\\site-packages')\n",
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def parseData(fname):\n",
    "    print(fname)\n",
    "    for l in open(fname, encoding=\"utf8\"):\n",
    "        yield eval(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/UCSD/CS_258/Assignment2/reviews.json\n"
     ]
    }
   ],
   "source": [
    "data = list(parseData(\"C:/UCSD/CS_258/Assignment2/reviews.json\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'review_id': 'NxL8SIC5yqOdnlXCg18IBg', 'user_id': 'KpkOkG6RIf4Ra25Lhhxf1A', 'business_id': '2aFiy99vNLklCx3T_tGS9A', 'stars': 5, 'date': '2011-10-10', 'text': \"If you enjoy service by someone who is as competent as he is personable, I would recommend Corey Kaplan highly. The time he has spent here has been very productive and working with him educational and enjoyable. I hope not to need him again (though this is highly unlikely) but knowing he is there if I do is very nice. By the way, I'm not from El Centro, CA. but Scottsdale, AZ.\", 'useful': 0, 'funny': 0, 'cool': 0, 'type': 'review'}\n",
      "100001\n"
     ]
    }
   ],
   "source": [
    "print(data[0])\n",
    "print (len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dic_rating = {}\n",
    "reviewers_dict = {}\n",
    "for d in data:\n",
    "    if not d['stars'] in dic_rating:\n",
    "        dic_rating[d['stars']] = 1\n",
    "    else:\n",
    "        dic_rating[d['stars']]  += 1 \n",
    "    \n",
    "    if not d['user_id'] in reviewers_dict:\n",
    "        reviewers_dict[d['user_id']] = 1\n",
    "    else:\n",
    "        reviewers_dict[d['user_id']] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Users are:\n",
      "63517\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Users are:\")\n",
    "print(len(reviewers_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{5: 37319, 4: 24724, 1: 15359, 3: 13822, 2: 8777}\n",
      "OrderedDict([(1, 15359), (2, 8777), (3, 13822), (4, 24724), (5, 37319)])\n"
     ]
    }
   ],
   "source": [
    "print(dic_rating)\n",
    "od = collections.OrderedDict(sorted(dic_rating.items()))\n",
    "print(od)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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2ALDIHjmghBT6tS0GgT2BnY2LGy9zr3a7Onq+UJKOudxmmz0pLX38YS5xE4DX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\nksT/BzuxZ/pAAM7XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15d3d300710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = '1', '2', '3', '4','5'\n",
    "sizes = [15359, 8777, 13822, 24724,37319]\n",
    "explode = (0, 0, 0, 0.1,0.1)\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',shadow=True, startangle=90)\n",
    "ax1.axis('equal')\n",
    "plt.savefig('rating_piechart.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15d3d300128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(od)), od.values(), align='center')\n",
    "plt.xticks(range(len(od)), od.keys())\n",
    "plt.xlabel('Ratings')\n",
    "plt.ylabel('Number of Reviews')\n",
    "plt.savefig('rating_histogram.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "359870\n",
      "3.5986640133598664\n"
     ]
    }
   ],
   "source": [
    "sum_rating = 0 \n",
    "for k in dic_rating:\n",
    "    sum_rating += int(k) * dic_rating[k]\n",
    "print(sum_rating)\n",
    "avg = sum_rating/100001\n",
    "print(sum_rating/100001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "error = 0 \n",
    "for d in data:\n",
    "    error += abs(d['stars'] - avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE with the baseline is: 1.2443693286883795\n"
     ]
    }
   ],
   "source": [
    "print ('MSE with the baseline is: ' + str(error/100001))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "posW = opinion_lexicon.positive()\n",
    "negW = opinion_lexicon.negative()\n",
    "score = defaultdict(int)\n",
    "for w in posW:\n",
    "    score[w] = 1\n",
    "for w in negW:\n",
    "    score[w] = -1\n",
    "    \n",
    "    \n",
    "def demo_liu_hu_lexicon(sentence):\n",
    "    tokenizer = treebank.TreebankWordTokenizer()\n",
    "    pos_words = 0\n",
    "    neg_words = 0\n",
    "    tokenized_sent = [score[word.lower()] for word in tokenizer.tokenize(sentence) ]\n",
    "    #print tokenized_sent\n",
    "    return sum(tokenized_sent)/(1+1.0*len(tokenized_sent))\n",
    "    #print tokenized_sent\n",
    "    #tokenized_sent = [word for word in tokenized_sent if word not in stopwords.words('english')]\n",
    "    '''\n",
    "    #print len(tokenized_sent)\n",
    "    for word in tokenized_sent:\n",
    "        if word in posW:\n",
    "            pos_words += 1\n",
    "        elif word in negW:\n",
    "            neg_words += 1\n",
    "\n",
    "    if pos_words > neg_words:\n",
    "        return 1\n",
    "    elif pos_words < neg_words:\n",
    "        return -1\n",
    "    elif pos_words == neg_words:\n",
    "        return 0\n",
    "    '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sent_feat(text,rating):\n",
    "    blob = TextBlob(text)\n",
    "    sent=0.0\n",
    "    for sentence in blob.sentences:\n",
    "        sent+=sentence.sentiment.polarity\n",
    "\n",
    "    if sent>0:\n",
    "        r=5-rating\n",
    "    else:\n",
    "        r=rating\n",
    "    return abs(sent)*r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-39c02505e7e0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1500\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdemo_liu_hu_lexicon\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'text'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msent_feat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'text'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'stars'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "x = 1500\n",
    "print(demo_liu_hu_lexicon(data[0]['text']))\n",
    "print(sent_feat(data[x]['text'], data[x]['stars']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "20000\n",
      "30000\n",
      "40000\n",
      "50000\n",
      "60000\n",
      "70000\n",
      "80000\n",
      "90000\n",
      "100000\n"
     ]
    }
   ],
   "source": [
    "sent_nltk = defaultdict(float)\n",
    "sent_tb = defaultdict(float)\n",
    "sent_nltk_sum = defaultdict(float)\n",
    "sent_tb_sum = defaultdict(float)\n",
    "\n",
    "iter = 0\n",
    "for d in data:\n",
    "    iter += 1\n",
    "    if iter % 10000 == 0:\n",
    "        print (iter)\n",
    "    #print iter\n",
    "    sent_nltk_sum[(d['review_id'], d['user_id'])] = demo_liu_hu_lexicon(d['text'])\n",
    "    sent_nltk[(d['review_id'], d['user_id'])] = demo_liu_hu_lexicon(d['text'])\n",
    "    sent_tb[(d['review_id'], d['user_id'])] = sent_feat(d['text'], d['stars'])\n",
    "    sent_tb_sum[(d['review_id'], d['user_id'])] = sent_feat(d['text'], d['stars'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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