notes/nlp/nlp_TFIDF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package words to /home/yui/nltk_data...\n",
      "[nltk_data]   Package words is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/yui/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /home/yui/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "0it [00:00, ?it/s]\n",
      "TqdmDeprecationWarning: Please use `tqdm.pandas(...)` instead of `tqdm_pandas(tqdm(...))`.\n",
      "/home/yui/anaconda3/lib/python3.7/site-packages/tqdm/std.py:658: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import Panel\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OriginalTweet</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>advice Talk to your neighbours family to excha...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coronavirus Australia: Woolworths to give elde...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>My food stock is not the only one which is emp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Me, ready to go at supermarket during the #COV...</td>\n",
       "      <td>Extremely Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       OriginalTweet           Sentiment\n",
       "0  @MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...             Neutral\n",
       "1  advice Talk to your neighbours family to excha...            Positive\n",
       "2  Coronavirus Australia: Woolworths to give elde...            Positive\n",
       "3  My food stock is not the only one which is emp...            Positive\n",
       "4  Me, ready to go at supermarket during the #COV...  Extremely Negative"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import math\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "from nltk import word_tokenize\n",
    "nltk.download('words')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from tqdm import tqdm,tqdm_pandas\n",
    "\n",
    "tqdm_pandas(tqdm())\n",
    "\n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "PATH=\"/home/yui/Documents/data/nlp/covidTweet/Corona_NLP_train.csv\"\n",
    "df = pd.read_csv(PATH, encoding = \"ISO-8859-1\")\n",
    "df = df[[\"OriginalTweet\",\"Sentiment\"]]\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Neutral', 'Positive', 'Extremely Negative', 'Negative',\n",
       "       'Extremely Positive'], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sentiment\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41157, 2)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class preprocessPipe:\n",
    "    def __init__(self):\n",
    "        self.eng = set(nltk.corpus.words.words())\n",
    "        self.lem = WordNetLemmatizer()\n",
    "        self.w2d = dict() # word:({docid:count,...},nt)\n",
    "        self.s2id = dict()\n",
    "    def get_wordnet_pos(self,word):\n",
    "        \"\"\"Map POS tag to first character lemmatize() accepts\"\"\"\n",
    "        tag = nltk.pos_tag([word])[0][1][0].upper()\n",
    "        tag_dict = {\"J\": wordnet.ADJ,\n",
    "                    \"N\": wordnet.NOUN,\n",
    "                    \"V\": wordnet.VERB,\n",
    "                    \"R\": wordnet.ADV}\n",
    "        return tag_dict.get(tag,wordnet.NOUN)\n",
    "    def lemmWord(self,w):\n",
    "        return self.lem.lemmatize(w,\n",
    "                self.get_wordnet_pos(w))\n",
    "    def tokenSentence(self,s):\n",
    "        return nltk.wordpunct_tokenize(s)\n",
    "    def sent2id(self,sentence):\n",
    "        ind = len(self.s2id)+1\n",
    "        self.s2id[sentence]=ind\n",
    "        return ind\n",
    "    def run(self,sentence):\n",
    "        sentence = sentence.lower()\n",
    "        ind = self.sent2id(sentence)\n",
    "        tokens = self.tokenSentence(sentence)\n",
    "        lemWords,visited,lenEng = [],set(),0\n",
    "        for w in tokens:\n",
    "            if w not in self.eng:\n",
    "                continue\n",
    "            w = self.lemmWord(w)\n",
    "            lemWords.append(w)\n",
    "            \n",
    "            # other operations\n",
    "            word2Doc,nt = self.w2d.get(w,(dict(),0))\n",
    "            if w not in visited:\n",
    "                nt+=1\n",
    "            freq = word2Doc.get(ind,0)+1\n",
    "            word2Doc[ind]=freq\n",
    "            self.w2d[w]=(word2Doc,nt)\n",
    "            visited.add(w)\n",
    "            lenEng+=1\n",
    "            \n",
    "        for w in visited:\n",
    "            word2Doc,nt = self.w2d[w]\n",
    "            p = word2Doc[ind]/lenEng\n",
    "            word2Doc[ind]=p\n",
    "            self.w2d[w] = (word2Doc,nt)\n",
    "            \n",
    "        return \" \".join(lemWords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pp = preprocessPipe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "41157it [00:55, 739.12it/s]\n"
     ]
    }
   ],
   "source": [
    "df[\"ProcessedTweet\"] = df[\"OriginalTweet\"]\\\n",
    "    .progress_apply(pp.run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Term frequency-Inverse document frequency\n",
    "- Term frequency\n",
    "$$tf(t,d)=\\frac{f_{t,d}}{\\sum_{t'\\in d}f_{t',d}}$$\n",
    "\n",
    "where $f_{t,d}$ the count of a term $t$ in a document $d$. \n",
    "\n",
    "|Scheme|Weight|\n",
    "|---|-----|\n",
    "|binary|0,1|\n",
    "|raw count|$f_{t,d}$|\n",
    "|term freq|$\\frac{f_{t,d}}{\\sum_{t'\\in d}f_{t',d}}$|\n",
    "|log norm|log(1+$f_{t,d}$)|\n",
    "|double norm .5|0.5+0.5$\\frac{f_{t,d}}{\\max_{t'\\in d}f_{t',d}}$|\n",
    "|double norm K|K+(1-K)$\\frac{f_{t,d}}{\\max_{t'\\in d}f_{t',d}}$|\n",
    "\n",
    "- Inverse document frequency\n",
    "$$idf(t,D) = \\log\\frac{N}{|\\{d\\in D:t\\in d\\}|}$$\n",
    "\n",
    "where $N$ total number of documents in the corpus $N=|D|$. $|\\{d\\in D:t\\in d\\}|$ number of documents where the term $t$ appears. If the term is not in the corpus, it can lead to a division-by-zero. It is therefore common to adjust the denominator to $1+|\\{d\\in D:t\\in d\\}|$. \n",
    "\n",
    "|Scheme|Weight|\n",
    "|---|---|\n",
    "|unary|1|\n",
    "|inverse document frequency|$\\log\\frac{N}{n_t}$|\n",
    "|inverse document frequency max|log$\\frac{\\max_{t'\\in d}n_{t'}}{n_t+1}$|\n",
    "|inverse document frequency smooth|log$\\frac{N}{n_t+1}$+1|\n",
    "|probabilistic inverse document frequency|log$\\frac{N-n_t}{n_t}$|\n",
    "\n",
    "https://en.wikipedia.org/wiki/Tf%E2%80%93idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotSent2WordAnalysis(label,i):\n",
    "    sent = df[df[\"Sentiment\"]==label][\"OriginalTweet\"].iloc[i].lower()\n",
    "    sid = pp.s2id[sent]\n",
    "    tokens = pp.tokenSentence(sent)\n",
    "    words,scores,Length = [],[],len(pp.s2id)\n",
    "    for w in tokens:\n",
    "        if w not in pp.eng:\n",
    "            continue\n",
    "        w = pp.lemmWord(w)\n",
    "        word2Doc,nt = pp.w2d[w]\n",
    "        words.append(w)\n",
    "        scores.append(word2Doc[sid]*math.log(Length/nt))\n",
    "    plt.barh(words,scores)\n",
    "    print(sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coronavirus australia: woolworths to give elderly, disabled dedicated shopping hours amid covid-19 outbreak https://t.co/binca9vp8p\n"
     ]
    },
    {
     "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": [
    "plotSent2WordAnalysis(\"Positive\",1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@10downingstreet @grantshapps what is being done to ensure food and other essential products are being re-stocked at supermarkets and panic buying actively discouraged? it cannot be left to checkout staff to police the actions of the selfish and profiteer\n"
     ]
    },
    {
     "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": [
    "plotSent2WordAnalysis(\"Negative\",1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "was at the supermarket today. didn't buy toilet paper. #rebel\r",
      "\r\n",
      "\r",
      "\r\n",
      "#toiletpapercrisis #covid_19 https://t.co/evxkqlidaz\n"
     ]
    },
    {
     "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": [
    "plotSent2WordAnalysis(\"Neutral\",1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#horningsea is a caring community. letâs all look after the less capable in our village and ensure they stay healthy. bringing shopping to their doors, help with online shopping and self isolation if you have symptoms or been exposed to somebody who has. https://t.co/lsgrxxhjhh\n"
     ]
    },
    {
     "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": [
    "plotSent2WordAnalysis(\"Extremely Positive\",1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "with 100  nations inficted with  covid  19  the world must  not  play fair with china  100 goverments must demand  china  adopts new guilde  lines on food safty  the  chinese  goverment  is guilty of  being  irosponcible   with life  on a global scale\n"
     ]
    },
    {
     "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": [
    "plotSent2WordAnalysis(\"Extremely Negative\",1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}