SME+-Try.ipynb

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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/liz/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
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   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/liz/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
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       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
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   "source": [
    "import itertools\n",
    "\n"
   ]
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['thoughts', 'bloggers', 'ghost', 'writers'], ['mean'], ['thing'], ['hell', 'warning', 'statement', 'hawks', 'gordhan'], ['hymn', 'weekend', 'starts', 'beyonc', 'saying'], ['loses', 'province', 'must', 'remember', 'sars', 'prasa', 'pravin', 'gordan', 'scandals', 'blaming', 'coalitions'], ['meet', 'girl', 'ugly', 'hairdo'], ['keeps', 'referring', 'brother'], ['degrees', 'find', 'mother', 'selling', 'popsicles', 'pula', 'cents'], ['supervisor', 'calls', 'spikes', 'hand'], ['people', 'drop', 'meetings', 'minutes', 'minutes', 'feel', 'like', 'something', 'important', 'came', 'nerve'], ['peoples', 'like', 'know', 'well', 'thinking'], ['someone', 'called', 'ready'], ['someone', 'much', 'shit', 'says', 'want', 'back', 'life'], ['trying', 'tell', 'something', 'completely', 'embarrassing', 'response', 'consists', 'multiple', 'friends', 'like'], ['taxi', 'driver', 'like'], ['funeral', 'reply', 'know', 'visnonce', 'fuck'], ['playing', 'child', 'play', 'bouncing', 'shit', 'like', 'wooah'], ['people', 'serious', 'relationship', 'exactly', 'makes', 'relationship'], ['name', 'thing', 'know', 'anwer', 'phone'], ['parents', 'give', 'beating', 'start', 'crying', 'like', 'crying', 'give', 'something', 'growing', 'black'], ['whenever', 'hear', 'brandy', 'must', 'full', 'moon', 'miss', 'high', 'school'], ['effortlessly', 'give', 'lend', 'diesel', 'timepiece', 'like', 'uthe', 'cela', 'ngim', 'dayisele', 'lona', 'bitch', 'please'], ['busy', 'also', 'hope', 'zuma', 'turns', 'charges', 'corruption'], ['white', 'monopoly', 'capital', 'begging', 'zuma', 'fire', 'gordhan', 'quite', 'puppet', 'masters', 'dressed'], ['white', 'capital', 'actually', 'loot', 'zero', 'rands', 'treasury', 'years', 'deliver', 'services', 'looted', 'enough', 'fool', 'stay', 'woke'], ['gordhan', 'investigated', 'corrupt', 'holds', 'business', 'interests', 'heart'], ['squirting', 'trending', 'laughing'], ['wife', 'tonight'], ['wise', 'words', 'never', 'abuse', 'women', 'skeem', 'saam'], ['original', 'black', 'care', 'free', 'girl', 'tweets', 'floating', 'time', 'line'], ['women', 'crazy', 'play', 'brought', 'baby', 'wilding', 'niggas', 'victims'], ['wonder', 'asking', 'always'], ['lecturer', 'says', 'look', 'register', 'know', 'attending', 'classes'], ['tiring', 'decisions', 'things', 'jacob', 'zuma', 'reason', 'vote', 'need', 'pryrs'], ['yebo', 'fuse', 'music', 'bear', 'witness', 'better', 'featuring', 'gentry', 'kozia', 'atwm'], ['yall', 'remember', 'tweet', 'girl', 'said', 'meet', 'thats', 'shit', 'finish'], ['yeah', 'slay'], ['yeah', 'today', 'think'], ['later', 'combi', 'earlier'], ['nasty', 'girl', 'nasty', 'tone', 'sies', 'maan'], ['busy', 'bragging', 'like', 'slept', 'last', 'night'], ['busy', 'telling', 'working', 'hard', 'cares', 'choice', 'keep'], ['want', 'miss', 'worship', 'quicke'], ['niggas', 'trying', 'expose', 'ayingo', 'defend'], ['know', 'account', 'regardless', 'position', 'sensitive', 'nature', 'hypocrisy'], ['must', 'listen', 'tunes', 'make', 'kranium', 'tory'], ['spelled', 'blind', 'wrong'], ['think', 'stop', 'headlines', 'protected', 'zuma', 'head', 'guilty'], ['need', 'cash', 'kicks', 'promoters'], ['opinion', 'useless'], ['zilla', 'malaysian', 'driftwood', 'medium', 'tank', 'decoration', 'free', 'shipping', 'cwuq'], ['zilla', 'slimline', 'tropical', 'fixture'], ['zuma', 'collective', 'responsibility', 'presidential', 'powers', 'behind', 'gordhan', 'market', 'white', 'privilege', 'behind'], ['zuma', 'acting', 'like', 'jilted', 'lover', 'rather', 'leave', 'killing', 'rand', 'vote'], ['zuma', 'minions', 'pravin', 'dear', 'minions', 'neither', 'hawks'], ['zuma', 'priority', 'jacob', 'zuma'], ['energy', 'news', 'next', 'zuma', 'list', 'nuclear', 'deal'], ['text', 'depleting', 'data', 'bundle', 'rates'], ['brother', 'killed', 'mhlongo', 'character', 'killed', 'cute', 'word'], ['unsaved', 'numbers', 'kibepzqvie'], ['many', 'came', 'took', 'hope', 'says', 'sharing', 'caring', 'baby'], ['song', 'like', 'made', 'hopsin', 'mind', 'hopsin', 'making', 'music', 'impression'], ['mine', 'would', 'essentially'], ['morning', 'wayde'], ['smell', 'needs', 'woolies', 'ayrshire', 'plain', 'yoghurt', 'howaru', 'cultures', 'woolies', 'seasonal', 'fruit', 'salad', 'water'], ['live', 'need', 'hear', 'hype'], ['call', 'size'], ['basically', 'mine'], ['good', 'trait', 'nice', 'interview'], ['eish', 'africa', 'school', 'zuma', 'would', 'head'], ['bunked', 'afrikaans', 'story', 'time', 'still', 'knew', 'stories', 'heart'], ['says', 'caster', 'google', 'name'], ['whole', 'petunia'], ['basically', 'kagiso', 'went', 'rough', 'queen', 'mzansi'], ['someone', 'asks', 'wrong'], ['eventually', 'chief'], ['dear', 'could', 'please', 'create', 'black', 'heart', 'emoji', 'express', 'true', 'emotions', 'useless', 'thank'], ['electricity', 'thing'], ['babe', 'said', 'word', 'venacular'], ['anyway'], ['call', 'flous', 'pascoe', 'peers'], ['elhae', 'wonder', 'woman', 'goes', 'beat', 'drops'], ['example', 'people', 'amnesia', 'gautrain', 'shilowa', 'premier', 'proposing', 'project', 'many', 'screamed', 'corruption', 'today'], ['threatened', 'beating', 'give', 'beating', 'crying', 'african', 'parents'], ['agree', 'trevor', 'manuel', 'pravin', 'gordhan', 'doomed', 'brah'], ['grown', 'calls', 'anyway'], ['laughing', 'people', 'replying', 'gifs', 'free', 'twitter', 'replies', 'people'], ['laughing', 'porn', 'last', 'thing', 'downloaded', 'onto', 'computer', 'question', 'answer', 'wednesday'], ['look', 'thando', 'must', 'spell', 'name', 'correctly', 'spell', 'peanutt', 'okay'], ['okay', 'argument', 'getting', 'tired', 'worn', 'clothes', 'justification', 'rape'], ['phone', 'rings', 'hello', 'someone'], ['playing', 'general', 'knowledge', 'class', 'color', 'starts', 'letter'], ['scrolled', 'thread', 'like', 'michael', 'dead', 'better', 'list'], ['think', 'competent', 'whites', 'blacks', 'country', 'timees', 'blacks', 'black', 'stoog'], ['girl', 'said', 'says'], ['jump', 'followers', 'direct', 'messages', 'deal', 'cause', 'usually', 'direct', 'message', 'first', 'unless', 'need', 'help'], ['like', 'pissing', 'sheets', 'posting', 'time', 'line', 'calling', 'squirt'], ['ever', 'curved', 'thinking', 'hope', 'nuts', 'leaves', 'pregnant', 'lmao'], ['guys', 'really', 'tell', 'people', 'good', 'good', 'continue', 'tell', 'crushing'], ['zuma', 'balls', 'court', 'cause', 'making', 'hawks', 'dirty', 'work', 'hands', 'pravin']]\n"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "import re, string, timeit\n",
    "import HTMLParser\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "html_parser = HTMLParser.HTMLParser()\n",
    "\n",
    "arrayofTweets = []\n",
    "\n",
    "#start replaceTwoOrMore\n",
    "def replaceTwoOrMore(s):\n",
    "    #look for 2 or more repetitions of character\n",
    "    pattern = re.compile(r\"(.)\\1{1,}\", re.DOTALL) \n",
    "    return pattern.sub(r\"\\1\\1\", s)\n",
    "#end\n",
    "\n",
    "with open(\"C:\\Anaconda2\\NewDataSet.txt\", \"r\") as ins:\n",
    "    for line in ins:\n",
    "        #Convert to lower case\n",
    "        begintweet = line.lower()\n",
    "        withoutescape = html_parser.unescape(begintweet)\n",
    "        decodedtweet = withoutescape.decode(\"utf8\").encode('ascii','ignore')\n",
    "        word1 = decodedtweet.replace(\"&amp\", \"&\")\n",
    "        word2 = word1.replace(\"'s'\", \" is \")\n",
    "        word3 = word2.replace(\"'ve\", \" have \")\n",
    "        word4 = word3.replace(\"'re\", \" are \")\n",
    "        word5 = word4.replace(\"n't\", \" not \")\n",
    "        wordfive = word5.replace(\"'ll\", \" will \")\n",
    "        wordsix = wordfive.replace(\"..\", \" \")\n",
    "        word6 = wordsix.replace(\"\\\\\", \" \")\n",
    "        word7 = word6.replace('\"', ' ')\n",
    "        #Convert www.* or https?://* to URL\n",
    "        word8 = re.sub(r\"http\\S+\", \" \", word7)\n",
    "        #Convert @username to AT_USER\n",
    "        word9= re.sub('@[^\\s]+',' ',word8)\n",
    "        #Replace #word with word\n",
    "        word10 =re.sub(r'\\n\\S+', ' ', word9)\n",
    "        word11 = re.sub(r'#([^\\s]+)', r' \\1 ', word10)\n",
    "        word12 = re.sub(r\"\\ud\\S+\", \" \", word11)\n",
    "        worded = re.sub(r\"\\u2\\S+\", \" \", word12)\n",
    "        worded1 = re.sub(r\"\\uf\\S+\", \" \", worded)\n",
    "        worded2 = re.sub(r\"\\\\\\S+\", \" \", worded1)\n",
    "        worded4 = re.sub( 't.co[^\\s]+',\" \",worded2 ) \n",
    "        worded5 = re.sub('\\n', ' ', worded4)\n",
    "        s = re.sub(r'[^\\w\\s]',' ',worded5) \n",
    "        s2 = re.sub(\"\\d\", \" \", s)\n",
    "        \n",
    "        #Remove additional white spaces\n",
    "        word14= re.sub('[\\s]+', ' ', s2)\n",
    "        \n",
    "        punctuation = list(string.punctuation)\n",
    "        stop = stopwords.words('english') + punctuation + ['rt', 'via']\n",
    "        \n",
    "        terms_stop = [term for term in word_tokenize(word14) if term not in stop]\n",
    "        arrayBefores = []\n",
    "    \n",
    "        for x in terms_stop:\n",
    "            if (len(x) > 3 and (x !='sa') and(x !='da')):\n",
    "                word13 = replaceTwoOrMore(x)\n",
    "                arrayBefores.append(x)\n",
    "        if(arrayBefores != []):\n",
    "            arrayofTweets.append(arrayBefores)\n",
    "\n",
    "\n",
    "print arrayofTweets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "categories = []\n",
    "isThere = False\n",
    "\n",
    "def equal(a, b):\n",
    "    # Ignore non-space and non-word characters\n",
    "    regex = re.compile(r'[^\\s\\w]')\n",
    "    return regex.sub('', a) == regex.sub('', b)\n",
    "\n",
    "def remove_duplicates(numbers):\n",
    "    newlist = []\n",
    "    for number in numbers:\n",
    "        if number not in newlist:\n",
    "            newlist.append(number)\n",
    "    return newlist\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------\n",
    "\n",
    "for line in arrayofTweets:\n",
    "    for word in line:\n",
    "        isThere = False\n",
    "        with open(\"C:\\Anaconda2\\synonymslist.txt\") as openfile:\n",
    "            for line2 in openfile:\n",
    "                for part in line2.split(','):\n",
    "                    if equal(word, part):\n",
    "                        isThere = True\n",
    "                        save = line2.split(',')[0]\n",
    "                        categories.append(save)\n",
    "                    break\n",
    "                        \n",
    "                \n",
    "        if(isThere is False):\n",
    "            with open(\"C:\\Anaconda2\\synonymslist.txt\") as openfile:\n",
    "                    for line2 in openfile:\n",
    "                        for part in line2.split():\n",
    "                            if equal(word, part):\n",
    "                                isThere = True\n",
    "                                save = line2.split(',')[0]\n",
    "                                categories.append(save)\n",
    "                            break\n",
    "            \n",
    "            if(isThere is False):\n",
    "                wordPut = word\n",
    "                categories.append(wordPut)\n",
    "            \n",
    "                \n",
    "categoriesNew = []\n",
    "categoriesNew = remove_duplicates(categories)\n",
    "\n",
    "print categoriesNew\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "categorysize  =  (len(categoriesNew) + 3)\n",
    "counter = 0\n",
    "\n",
    "categoryPos = categorysize - 3\n",
    "categoryNeg = categorysize - 2\n",
    "categoryNeu = categorysize - 1\n",
    "\n",
    "#print categorysize\n",
    "#print \"This is category vector size\"\n",
    "\n",
    "listOfVectors= []\n",
    "\n",
    "for i in range(categorysize):\n",
    "        vector = [0.0] * categorysize\n",
    "\n",
    "def findIndex(cat):\n",
    "    foundIndex = categoriesNew.index(cat) if cat in categoriesNew else -1\n",
    "    return foundIndex\n",
    "\n",
    "def checkSentiment(sentiment):\n",
    "    sent = sentiment\n",
    "    if sent == 'Positive':\n",
    "        return categoryPos\n",
    "    elif sent =='Negative':\n",
    "        return categoryNeg\n",
    "    else:\n",
    "        return categoryNeu\n",
    "    \n",
    "vector = []\n",
    "isThere = False\n",
    "\n",
    "for tweets in arrayofTweets:\n",
    "    vector = [0.0] * categorysize\n",
    "    for word in tweets:\n",
    "        isThere = False\n",
    "        with open(\"C:\\Anaconda2\\synonymslist.txt\") as openfile:\n",
    "            for line2 in openfile:\n",
    "                for part in line2.split(','):\n",
    "                    if equal(word, part):\n",
    "                        isThere = True\n",
    "                        cat = line2.split(',')[0]\n",
    "                        sentiment = line2.split(',')[-1]\n",
    "                        x = findIndex(cat)\n",
    "                        vector[x] = vector[x] + 1.0\n",
    "                        sentInd = checkSentiment(sentiment)\n",
    "                        vector[sentInd] = vector[sentInd] + 1.0\n",
    "                    break           \n",
    "                \n",
    "                        \n",
    "                        \n",
    "        if(isThere is False):\n",
    "            x = findIndex(word)\n",
    "            if (x != -1):\n",
    "                vector[x] = vector[x] + 1.0\n",
    "                vector[categoryNeu] = vector[categoryNeu] + 1.0\n",
    "                counter = counter + 1\n",
    "               \n",
    "        \n",
    "                \n",
    "            if (x == -1):\n",
    "                with open(\"C:\\Anaconda2\\synonymslist.txt\") as openfile:\n",
    "                    for line2 in openfile:\n",
    "                        for part in line2.split():\n",
    "                            if equal(word, part):\n",
    "                                isThere = True\n",
    "                                cat = line2.split(',')[0]\n",
    "                                sentiment = line2.split(',')[-1]\n",
    "                                x = findIndex(cat)\n",
    "                                vector[x] = vector[x] + 1.0\n",
    "                                sentInd = checkSentiment(sentiment)\n",
    "                                vector[sentInd] = vector[sentInd] + 1.0\n",
    "                            break\n",
    "            \n",
    "\n",
    "    listOfVectors.append(vector)\n",
    "    vector = [0.0] * categorysize\n",
    "    \n",
    "    \n",
    "print listOfVectors[0]\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 22] invalid mode ('w') or filename: 'C:\\\\Anaconda2\\testing.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-137-c7aebf8fc644>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0morig_stdout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'C:\\Anaconda2\\testing.txt'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIOError\u001b[0m: [Errno 22] invalid mode ('w') or filename: 'C:\\\\Anaconda2\\testing.txt'"
     ]
    }
   ],
   "source": [
    "from itertools import chain\n",
    "c=[]\n",
    "myCollection = []\n",
    "\n",
    "for x in listOfVectors:\n",
    "    c = list(itertools.chain.from_iterable(zip(x,categoriesNew)))\n",
    "    myCollection.append(c)\n",
    "print myCollection[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import string\n",
    "np.seterr(divide='ignore', invalid='ignore')\n",
    "np.set_printoptions(threshold=np.inf)\n",
    "\n",
    "normalizedVectors = []\n",
    "a = np.empty((0))\n",
    "indexBig = 0\n",
    "indexSmall = 0\n",
    "index = 0 \n",
    "indexOne = 0\n",
    "listOne = []\n",
    "listTwo = []\n",
    "NewListofLists = []\n",
    "ListofLists = []\n",
    "\n",
    "#normalize per whole list\n",
    "# normalization\n",
    "#categorysize = 2930\n",
    "#number of tweets = 993\n",
    "while index < categorysize:\n",
    "    for x in listOfVectors:\n",
    "        listOne.append(x[indexSmall])\n",
    "    NewListofLists.append(listOne)\n",
    "    listOne = []\n",
    "    indexSmall = indexSmall + 1\n",
    "    index = index + 1\n",
    "        \n",
    "for x in NewListofLists:\n",
    "    x_np = np.asarray(x)\n",
    "    np_minmax = (x_np - x_np.min()) / (x_np.max() - x_np.min())\n",
    "    np_minmax = np_minmax.astype('float')\n",
    "    np_minmax[np.isnan(np_minmax)] = 0\n",
    "    normalizedVectors.append(np_minmax)        \n",
    "        \n",
    "ourLength = len(listOfVectors)\n",
    "\n",
    "        \n",
    "while indexOne < ourLength:\n",
    "    for y in normalizedVectors:\n",
    "        listTwo.append(y[indexBig])\n",
    "    ListofLists.append(listTwo)\n",
    "    listTwo = []\n",
    "    indexOne = indexOne + 1\n",
    "    indexBig = indexBig + 1\n",
    "    \n",
    "import sys\n",
    "orig_stdout = sys.stdout\n",
    "f = file('C:\\Anaconda2\\testing.txt', 'w')\n",
    "sys.stdout = f\n",
    "\n",
    "for zee in ListofLists:\n",
    "    print zee, '\\n'\n",
    "\n",
    "sys.stdout = orig_stdout\n",
    "f.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  }
 ],
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