1.bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import load_corpus, stopwords\n",
    "\n",
    "TRAIN_PATH = \"./data/weibo2018/train.txt\"\n",
    "TEST_PATH = \"./data/weibo2018/test.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /var/folders/rt/khjltk4j6n78x9x3f20hdr6m0000gp/T/jieba.cache\n",
      "Loading model cost 1.023 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    }
   ],
   "source": [
    "# 分别加载训练集和测试集\n",
    "train_data = load_corpus(TRAIN_PATH)\n",
    "test_data = load_corpus(TEST_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>words</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>书中 自有 黄金屋 书中 自有 颜如玉 沿着 岁月 的 长河 跋涉 或是 风光旖旎 或是 姹...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>这是 英超 被 黑 的 最惨 的 一次 二哈 二哈 十几年来 中国 只有 孙继海 董方卓 郑...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中国 远洋 海运 集团 副总经理 俞曾 港 月 日 在 上 表示 中央 企业 走 出去 是 ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>看 流星花园 其实 也 还好 啦 现在 的 观念 以及 时尚 眼光 都 不一样 了 或许 十...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>汉武帝 的 罪己 诏 的 真实性 尽管 存在 着 争议 然而 轮台 罪己 诏 作为 中国 历...</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                                               words  label\n",
       "0  书中 自有 黄金屋 书中 自有 颜如玉 沿着 岁月 的 长河 跋涉 或是 风光旖旎 或是 姹...      1\n",
       "1  这是 英超 被 黑 的 最惨 的 一次 二哈 二哈 十几年来 中国 只有 孙继海 董方卓 郑...      0\n",
       "2  中国 远洋 海运 集团 副总经理 俞曾 港 月 日 在 上 表示 中央 企业 走 出去 是 ...      1\n",
       "3  看 流星花园 其实 也 还好 啦 现在 的 观念 以及 时尚 眼光 都 不一样 了 或许 十...      1\n",
       "4  汉武帝 的 罪己 诏 的 真实性 尽管 存在 着 争议 然而 轮台 罪己 诏 作为 中国 历...      1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_train = pd.DataFrame(train_data, columns=[\"words\", \"label\"])\n",
    "df_test = pd.DataFrame(test_data, columns=[\"words\", \"label\"])\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征编码（词袋模型）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/albertdxq/opt/anaconda3/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:383: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['元', '吨', '数', '末'] not in stop_words.\n",
      "  warnings.warn('Your stop_words may be inconsistent with '\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vectorizer = CountVectorizer(token_pattern='\\[?\\w+\\]?', \n",
    "                             stop_words=stopwords)\n",
    "X_train = vectorizer.fit_transform(df_train[\"words\"])\n",
    "y_train = df_train[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = vectorizer.transform(df_test[\"words\"])\n",
    "y_test = df_test[\"label\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型&测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "clf = MultinomialNB()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在测试集上用模型预测结果\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.80      0.78       155\n",
      "           1       0.91      0.88      0.89       345\n",
      "\n",
      "    accuracy                           0.86       500\n",
      "   macro avg       0.83      0.84      0.83       500\n",
      "weighted avg       0.86      0.86      0.86       500\n",
      "\n",
      "准确率: 0.856\n"
     ]
    }
   ],
   "source": [
    "# 测试集效果检验\n",
    "from sklearn import metrics\n",
    "\n",
    "print(metrics.classification_report(y_test, y_pred))\n",
    "print(\"准确率:\", metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 手动输入句子，判断情感倾向"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import processing\n",
    "\n",
    "strs = [\"终于收获一个最好消息\", \"哭了, 今天怎么这么倒霉\"]\n",
    "words = [processing(s) for s in strs]\n",
    "vec = vectorizer.transform(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = clf.predict(vec)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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