nlp_kelas_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/febriyanto/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\"\"Hello Mr. Smith, how are you doing today? The weather is great, and city is awesome. The sky is pinkish-blue. You shouldn't eat cardboard \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import sent_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_text = sent_tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello Mr. Smith, how are you doing today?',\n",
       " 'The weather is great, and city is awesome.',\n",
       " 'The sky is pinkish-blue.',\n",
       " \"You shouldn't eat cardboard\"]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_word = word_tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hello', 'Mr.', 'Smith', ',', 'how', 'are', 'you', 'doing', 'today', '?', 'The', 'weather', 'is', 'great', ',', 'and', 'city', 'is', 'awesome', '.', 'The', 'sky', 'is', 'pinkish-blue', '.', 'You', 'should', \"n't\", 'eat', 'cardboard']\n"
     ]
    }
   ],
   "source": [
    "print(tokenized_word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "text2 = \"Halo nama saya kenny. Saya suka main bola.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Halo', 'nama', 'saya', 'kenny', '.', 'Saya', 'suka', 'main', 'bola', '.']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tokenize(text2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Hello', 'Mr.', 'Smith', ',', 'how', 'are', 'you', 'doing', 'today', '?'], ['The', 'weather', 'is', 'great', ',', 'and', 'city', 'is', 'awesome', '.'], ['The', 'sky', 'is', 'pinkish-blue', '.'], ['You', 'should', \"n't\", 'eat', 'cardboard']]\n"
     ]
    }
   ],
   "source": [
    "sword = []\n",
    "for sent in tokenized_text:\n",
    "    sword.append(word_tokenize(sent))\n",
    "\n",
    "print(sword)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Hello', 'Mr.', 'Smith', ',', 'how', 'are', 'you', 'doing', 'today', '?'],\n",
       " ['The', 'weather', 'is', 'great', ',', 'and', 'city', 'is', 'awesome', '.'],\n",
       " ['The', 'sky', 'is', 'pinkish-blue', '.'],\n",
       " ['You', 'should', \"n't\", 'eat', 'cardboard']]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.probability import FreqDist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdist = FreqDist(tokenized_word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'is': 3, ',': 2, 'The': 2, '.': 2, 'Hello': 1, 'Mr.': 1, 'Smith': 1, 'how': 1, 'are': 1, 'you': 1, ...})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fdist.plot(30, cumulative=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/febriyanto/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words(\"english\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello',\n",
       " 'Mr.',\n",
       " 'Smith',\n",
       " ',',\n",
       " 'today',\n",
       " '?',\n",
       " 'The',\n",
       " 'weather',\n",
       " 'great',\n",
       " ',',\n",
       " 'city',\n",
       " 'awesome',\n",
       " '.',\n",
       " 'The',\n",
       " 'sky',\n",
       " 'pinkish-blue',\n",
       " '.',\n",
       " 'You',\n",
       " \"n't\",\n",
       " 'eat',\n",
       " 'cardboard']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_sent = []\n",
    "for word in tokenized_word:\n",
    "    if word not in stop_words:\n",
    "        filtered_sent.append(word)\n",
    "\n",
    "filtered_sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /home/febriyanto/nltk_data...\n",
      "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('averaged_perceptron_tagger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Hello', 'NNP'),\n",
       " ('Mr.', 'NNP'),\n",
       " ('Smith', 'NNP'),\n",
       " (',', ','),\n",
       " ('how', 'WRB'),\n",
       " ('are', 'VBP'),\n",
       " ('you', 'PRP'),\n",
       " ('doing', 'VBG'),\n",
       " ('today', 'NN'),\n",
       " ('?', '.'),\n",
       " ('The', 'DT'),\n",
       " ('weather', 'NN'),\n",
       " ('is', 'VBZ'),\n",
       " ('great', 'JJ'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('city', 'NN'),\n",
       " ('is', 'VBZ'),\n",
       " ('awesome', 'JJ'),\n",
       " ('.', '.'),\n",
       " ('The', 'DT'),\n",
       " ('sky', 'NN'),\n",
       " ('is', 'VBZ'),\n",
       " ('pinkish-blue', 'JJ'),\n",
       " ('.', '.'),\n",
       " ('You', 'PRP'),\n",
       " ('should', 'MD'),\n",
       " (\"n't\", 'RB'),\n",
       " ('eat', 'VB'),\n",
       " ('cardboard', 'NN')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_tag_word = nltk.pos_tag(tokenized_word)\n",
    "pos_tag_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Hello', 'Mr.', 'Smith', 'are', 'doing', 'today'],\n",
       " ['weather', 'is', 'great', 'city', 'is', 'awesome'],\n",
       " ['sky', 'is', 'pinkish-blue'],\n",
       " ['eat', 'cardboard']]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_tag_used = ['NN', 'NNP', 'VBP', 'VBZ', 'VBG', 'JJ']\n",
    "filtered_sent3 = []\n",
    "for arr_word in sword:\n",
    "    temp_sent = []\n",
    "    for word in arr_word:\n",
    "        if nltk.pos_tag([word])[0][1] in pos_tag_used:\n",
    "            temp_sent.append(nltk.pos_tag([word])[0][0])\n",
    "    filtered_sent3.append(temp_sent)\n",
    "\n",
    "filtered_sent3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1, 'A')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('Hello', 'NNP'),\n",
       "  ('Mr.', 'NNP'),\n",
       "  ('Smith', 'NNP'),\n",
       "  (',', ','),\n",
       "  ('how', 'WRB'),\n",
       "  ('are', 'VBP'),\n",
       "  ('you', 'PRP'),\n",
       "  ('doing', 'VBG'),\n",
       "  ('today', 'NN'),\n",
       "  ('?', '.')],\n",
       " [('The', 'DT'),\n",
       "  ('weather', 'NN'),\n",
       "  ('is', 'VBZ'),\n",
       "  ('great', 'JJ'),\n",
       "  (',', ','),\n",
       "  ('and', 'CC'),\n",
       "  ('city', 'NN'),\n",
       "  ('is', 'VBZ'),\n",
       "  ('awesome', 'JJ'),\n",
       "  ('.', '.')],\n",
       " [('The', 'DT'),\n",
       "  ('sky', 'NN'),\n",
       "  ('is', 'VBZ'),\n",
       "  ('pinkish-blue', 'JJ'),\n",
       "  ('.', '.')],\n",
       " [('You', 'PRP'),\n",
       "  ('should', 'MD'),\n",
       "  (\"n't\", 'RB'),\n",
       "  ('eat', 'VB'),\n",
       "  ('cardboard', 'NN')]]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_tag_sent = []\n",
    "for sent in sword:\n",
    "    pos_tag_sent.append(nltk.pos_tag(sent))\n",
    "pos_tag_sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Hello', 'Mr.', 'Smith', 'are', 'doing', 'today'],\n",
       " ['weather', 'is', 'great', 'city', 'is', 'awesome'],\n",
       " ['sky', 'is', 'pinkish-blue'],\n",
       " ['cardboard']]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sword = []\n",
    "for sent in tokenized_text:\n",
    "    sword.append(word_tokenize(sent))\n",
    "\n",
    "pos_tag_sent = []\n",
    "for sent in sword:\n",
    "    pos_tag_sent.append(nltk.pos_tag(sent))\n",
    "\n",
    "pos_tag_used = ['NN', 'NNP', 'VBP', 'VBZ', 'VBG', 'JJ']\n",
    "var = []\n",
    "for arr_word in pos_tag_sent:\n",
    "    temp_sent = []\n",
    "    for tupel in arr_word:\n",
    "        if tupel[1] in pos_tag_used:\n",
    "            temp_sent.append(tupel[0])\n",
    "    var.append(temp_sent)\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Hello', 'NN')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.pos_tag(['Hello'])[0]"
   ]
  },
  {
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}