pon-x/形態素解析2.ipynb

## 形態素解析2.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 自然言語処理\n",
    "\n",
    "## MeCabをつかって、形態素解析しよう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "青空文庫より、<br>\n",
    "吾輩は猫である　→　https://www.aozora.gr.jp/cards/000148/card789.html<br>\n",
    "こころ　　　　　→　https://www.aozora.gr.jp/cards/000148/card773.html<br>\n",
    "のテキストファイルをダウンロードして以下を行ってください。<br>\n",
    "<br>\n",
    "1.ファイルを取り込み、形態素解析を行ってください。<br>\n",
    "2.品詞は名詞のみ、ただし1文字の単語は除外してください。<br>\n",
    "3.出現頻度をカウントして、上位10件を下り順にソートし、グラフ化してください。<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import MeCab\n",
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class Aozora:\n",
    "    \n",
    "    def __init__(self ,path): \n",
    "        \n",
    "        with open(path) as f: # ファイルを読み込みます。\n",
    "            self.text = f.read()\n",
    "        \n",
    "        self.doc = [] # 出現ワードをためておく箱を初期化する\n",
    "    \n",
    "    def words(self):\n",
    "        \n",
    "        tagger = MeCab.Tagger()\n",
    "        \n",
    "        for s in tagger.parse(self.text).split(\"\\n\"):\n",
    "            \n",
    "            word = s.split(\"\\t\")[0]\n",
    "\n",
    "            if word == \"EOS\":\n",
    "                break\n",
    "            else:\n",
    "                part = s.split(\"\\t\")[1].split(\",\")[0]\n",
    "                if part in [\"名詞\"]: # 名詞である\n",
    "                    if len(word) > 1: # 2文字以上である\n",
    "                         self.doc.append(word) # ワードを保存\n",
    "\n",
    "\n",
    "        dic = {} # 辞書を作る\n",
    "        for s in self.doc:\n",
    "            if s in dic:\n",
    "                dic[s] += 1 #辞書に既にあればインクリメント\n",
    "            else:\n",
    "                dic[s] = 1 #辞書になければデータを追加\n",
    "\n",
    "        dic_s = pd.Series(dic)\n",
    "        \n",
    "        dic_s = dic_s.sort_values()[-10:] # 辞書内の頻度トップ10を抽出\n",
    "        \n",
    "        return dic_s.sort_values()\n",
    "\n",
    "    def graph(self, dic_s): # グラフ描画\n",
    "        \n",
    "        fig = plt.figure(figsize=(10, 6))\n",
    "        ax = fig.add_subplot()\n",
    "\n",
    "        ax.barh(dic_s.keys(),dic_s.tolist())\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"wagahaiwa_nekodearu.txt\"\n",
    "neko = Aozora(path)\n",
    "w_neko = neko.words()\n",
    "\n",
    "path = \"kokoro.txt\"\n",
    "kokoro = Aozora(path)\n",
    "w_kokoro = kokoro.words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neko.graph(w_neko)\n",
    "\n",
    "kokoro.graph(w_kokoro)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}