Explore-Gossiping-Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# PTT 中文語料\n",
    "\n",
    "@Justin Yang\n",
    "\n",
    "嗨，這裡是 PTT 中文語料集，蒐集自 PTT 八卦版於 2015 年至 2017 年 6 月的文章，我透過[某些假設與方法](https://github.com/zake7749/PTT-Chat-Generator)將每篇文章化簡為問答配對，其中問題來自文章的標題，而回覆是該篇文章的推文。\n",
    "不過目前這份資料集的噪聲還有點大，如果您發現有更棒的問答配對提取方法，還請與我聯繫。接下來我會簡單的說明一下這份資料集的資訊，祝各位開發順利 :>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 資料格式\n",
    "\n",
    "資料集中每一行都是一個問答配對，問與答之間以一個 tab (`\\t`) 區隔開，比如說:\n",
    "\n",
    "```\n",
    "matlab有什麼炫砲一點的圖？\t一樣的圖改一改顏色，有點半透明感覺更唬爛炫\n",
    "有沒有情人節吃什麼cp值最高的八卦\t吃屎啊廢話 免費的一餐\n",
    "姆咪一個人守得住街亭嗎?\t引來一堆肥宅穢土轉生 有機會喔\n",
    "有沒有被落石砸到該反省的八卦\t蔡英文執政就故意誇大報導 東森不意外\n",
    "情人節該帶女朋友去哪慶祝？\t用了一整年 對她好一點  送專業乾洗店吧\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "問答配對筆數: 418202\n"
     ]
    }
   ],
   "source": [
    "# a simple demo for loading dataset.\n",
    "\n",
    "qa_pairs = []\n",
    "\n",
    "with open('data/Gossiping-QA-Dataset.txt', 'r', encoding='utf-8') as dataset:\n",
    "    for line in dataset:\n",
    "        line = line.strip('\\n')\n",
    "        q,a = line.split('\\t')\n",
    "        qa_pairs.append([q,a])\n",
    "print(\"問答配對筆數:\",len(qa_pairs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索資料集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q: 有沒有桶仔雞的八卦?\nA: 裡面其實不是雞肉喔，嘻嘻嘻~~~\nQ: 新垣結衣吃晚餐\nA: 醒醒好嗎?   我去問問\nQ: 有沒有圖書館唸書的八掛？\nA: 暑假去圖書館還有在裡面睡覺在椅子或沙發上的\nQ: 鍋貼要吃八方雲集還是四海遊龍\nA: 當然是四海遊龍 每比一次國際賽事吃一次\nQ: 有沒有男生比較容易維持體態的八卦？\nA: 女生可以靠少吃維持體態 男生沒辦法 有工作更難\n"
     ]
    }
   ],
   "source": [
    "# randomly print the qa pairs\n",
    "import random\n",
    "\n",
    "for i in range(5):\n",
    "    idx = random.randint(0, len(qa_pairs))\n",
    "    print(\"Q:\", qa_pairs[idx][0])\n",
    "    print(\"A:\", qa_pairs[idx][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the distribution of sentences' length.\n",
    "\n",
    "from collections import defaultdict\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "q_lens = defaultdict(int)\n",
    "a_lens = defaultdict(int)\n",
    "total_lens = defaultdict(int)\n",
    "\n",
    "for q,a in qa_pairs:\n",
    "    \n",
    "    length_q = len(q)\n",
    "    length_a = len(a)\n",
    "    \n",
    "    q_lens[length_q] += 1\n",
    "    a_lens[length_a] += 1\n",
    "    \n",
    "    total_lens[length_q] += 1\n",
    "    total_lens[length_a] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_distribution(ctr, title):\n",
    "    \n",
    "    plt.bar(range(len(ctr)), ctr.values(), align='center')\n",
    "    plt.title(title)\n",
    "    plt.xticks(range(len(ctr)), ctr.keys())\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wdCVvEPDH7N+lwG2V/pieZV7J5T6z0h8PZt7LwJOVvLfm+rEk14UZ5PoM7EtZ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\n1Tg4mJlZzf8HrSGvW4ygLuIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e6bd694a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(q_lens, \"The length distribution of Questions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QtKBUdpakpZLmpWlCad3pktolPS7p4FL5+FTWLmlaqXwHSfem8uskrd+TB2hmZp1X5crh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\nz/iH9/MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e51373f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(a_lens, \"The length distribution of Answers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s0yYC38n3ndSvHOZROscVrD1MdRflDPoWyoH3kRZtaHSse5uW/3fZjlcoPyMA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e51206080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(total_lens, \"The length distribution of All\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Question without reply 650\n"
     ]
    }
   ],
   "source": [
    "# show the number of questions which do not have any reply.\n",
    "\n",
    "nb_no_reply = 0\n",
    "\n",
    "for q,a in qa_pairs:\n",
    "    if a == '沒有資料':\n",
    "        nb_no_reply += 1\n",
    "print(\"# Question without reply\", nb_no_reply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}