nlp_day_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisis Sentimen\n",
    "\n",
    "### Workflow\n",
    "- Text > Sentiment Classification > positif, negatif, netral\n",
    "- Text > Subjectivity Class > Netral/Subjek ~(if Subjek > Sentiment Classification > positif, negatif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SentiWordNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package sentiwordnet to\n",
      "[nltk_data]     /home/febriyanto/nltk_data...\n",
      "[nltk_data]   Package sentiwordnet is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /home/febriyanto/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('sentiwordnet')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SentiWordNet can be imported like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import sentiwordnet as swn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SentiSynsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "breakdown = swn.senti_synset('breakdown.n.03')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<breakdown.n.03: PosScore=0.0 NegScore=0.25>\n"
     ]
    }
   ],
   "source": [
    "print(breakdown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breakdown.pos_score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breakdown.neg_score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breakdown.obj_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'SentiSynset' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-53c5ef6d1521>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mswn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msenti_synsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'slow'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# doctest: +NORMALIZE_WHITESPACE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m [SentiSynset('decelerate.v.01'), SentiSynset('slow.v.02'),\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mSentiSynset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'slow.v.03'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSentiSynset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'slow.a.01'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mSentiSynset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'slow.a.02'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSentiSynset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'slow.a.04'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m SentiSynset('slowly.r.01'), SentiSynset('behind.r.03')]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'SentiSynset' is not defined"
     ]
    }
   ],
   "source": [
    "list(swn.senti_synsets('slow')) # doctest: +NORMALIZE_WHITESPACE\n",
    "[SentiSynset('decelerate.v.01'), SentiSynset('slow.v.02'),\n",
    "SentiSynset('slow.v.03'), SentiSynset('slow.a.01'),\n",
    "SentiSynset('slow.a.02'), SentiSynset('slow.a.04'),\n",
    "SentiSynset('slowly.r.01'), SentiSynset('behind.r.03')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "happy = swn.senti_synsets('happy', 'a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "all = swn.all_senti_synsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aspect Based Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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