From da65298f86b8774ba0c4eeec7e47ab1b53a33cb7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=90=B4=E6=B2=82=E9=92=8A?= <13190667+Yizhao_Wu4926@user.noreply.gitee.com> Date: Sat, 15 Jul 2023 14:47:46 +0000 Subject: [PATCH] =?UTF-8?q?=E8=BF=90=E8=A1=8C=E8=B4=AD=E7=89=A9=E8=AF=84?= =?UTF-8?q?=E8=AE=BA=E7=9A=84=E6=83=85=E6=84=9F=E5=88=86=E6=9E=90=E7=9A=84?= =?UTF-8?q?=E4=B8=BB=E7=A8=8B=E5=BA=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 吴沂钊 <13190667+Yizhao_Wu4926@user.noreply.gitee.com> --- 购物评论 情感分类/main.ipynb | 828 +++++++++++++++++++++++++++++++++++ 1 file changed, 828 insertions(+) create mode 100644 购物评论 情感分类/main.ipynb diff --git a/购物评论 情感分类/main.ipynb b/购物评论 情感分类/main.ipynb new file mode 100644 index 0000000..c55d334 --- /dev/null +++ b/购物评论 情感分类/main.ipynb @@ -0,0 +1,828 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# 导入需要的包\n", + "from collections import defaultdict\n", + "import pandas as pd\n", + "import re\n", + "import jieba\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import classification_report\n", + "from gensim.models.word2vec import Word2Vec\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rawy
0感觉用这个手机下载MP3听很方便，用T-Flash卡装上歌，就可以当一个小MP3用了，很不错...1
1外观美观，速度也不错。上面一排触摸键挺实用。应该对得起这个价格。当然再降点大家肯定也不反对。...1
2我刚拿到书，也没有仔细阅读，就是粗粗的翻了点，觉得还行。里面是蓝黑两种颜色的，有些单词的下面...1
3对于二胡曲的精选，应该出版一个系列套装，可分别出售，单独购买。精品二胡曲是有年代和阶段性的，...1
4用了一年半的 e680 终于送小偷了。刚刚买了一台e850，用了三天，说说我自己的感受。1：...1
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" + ], + "text/plain": [ + " raw y\n", + "0 感觉用这个手机下载MP3听很方便，用T-Flash卡装上歌，就可以当一个小MP3用了，很不错... 1\n", + "1 外观美观，速度也不错。上面一排触摸键挺实用。应该对得起这个价格。当然再降点大家肯定也不反对。... 1\n", + "2 我刚拿到书，也没有仔细阅读，就是粗粗的翻了点，觉得还行。里面是蓝黑两种颜色的，有些单词的下面... 1\n", + "3 对于二胡曲的精选，应该出版一个系列套装，可分别出售，单独购买。精品二胡曲是有年代和阶段性的，... 1\n", + "4 用了一年半的 e680 终于送小偷了。刚刚买了一台e850，用了三天，说说我自己的感受。1：... 1" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 读入数据\n", + "df_pos = pd.read_excel(r\"data/购物评论.xlsx\", sheet_name=\"正向\", header=None)\n", + "df_pos[\"y\"] = 1\n", + "df_neg = pd.read_excel(r\"data/购物评论.xlsx\", sheet_name=\"负向\", header=None)\n", + "df_neg[\"y\"] = 0\n", + "# 将正样本和负样本拼接在一起\n", + "df = df_pos.append(df_neg, ignore_index=True)\n", + "df.columns = [\"raw\", \"y\"]\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 [感觉, 用, 这个, 手机, 下载, MP3, 听, 很, 方便, ，, 用, T, -,...\n", + "1 [外观, 美观, ，, 速度, 也, 不错, 。, 上面, 一排, 触摸, 键, 挺, 实用...\n", + "2 [我刚, 拿到, 书, ，, 也, 没有, 仔细阅读, ，, 就是, 粗粗, 的, 翻, 了...\n", + "3 [对于, 二胡曲, 的, 精选, ，, 应该, 出版, 一个系列, 套装, ，, 可, 分别...\n", + "4 [用, 了, 一年, 半, 的, , e680, , 终于, 送, 小偷, 了, 。, ...\n", + "Name: raw, dtype: object" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 数据清洗\n", + "# 由于有些情感词本身也是停用词。因此这里仅去掉换行和空字符，不做其他的清理工作，以保留情感词。\n", + "df_cut = df[\"raw\"].apply(lambda x: re.sub('\\n', '', x))\n", + "# 将清洗后的文本用jieba.lcut转换为list形式\n", + "df_cut = df_cut.apply(jieba.lcut)\n", + "\n", + "df_cut.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "# 基于词典的情感分析\n", + "# 读入并创建情感词典\n", + "sentiment_dict = defaultdict(float) # 方便处理不存在于词典中的关键字\n", + "# 使用with open以节约内存\n", + "with open(r\"data\\BosonNLP_sentiment_score.txt\", \"r\", encoding=\"utf-8\") as sen_file:\n", + " for line in sen_file:\n", + " try:\n", + " key, value = line.split(\" \") # 直接运行会有奇怪的问题出现\n", + " except Exception:\n", + " # 直接跳过有问题的行\n", + " pass\n", + " else:\n", + " sentiment_dict[key] = float(value) # 正常情况" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 21.581138\n", + "1 14.952362\n", + "2 6.245357\n", + "3 30.980969\n", + "4 36.519372\n", + "Name: raw, dtype: float64" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 以最简单的直接匹配的方式进行情感打分\n", + "def get_score(words):\n", + " return sum(sentiment_dict[word] for word in words)\n", + "\n", + "\n", + "y = df_cut.apply(get_score)\n", + "\n", + "y.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "该模型的准确率为： 0.6764648722184433\n" + ] + } + ], + "source": [ + "# 根据分数>0为正向、分数<0为负向判定准确率\n", + "precise = lambda x: (df[\"y\"][x] == 1 and score[x] > 0) or (df[\"y\"][x] == 0 and score[x] <= 0)\n", + "# 准确度评估\n", + "print(\"该模型的准确度为：\", sum(1 for i in range(len(df)) if precise(i)) / len(df))" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "我谢谢蒙牛！多一层体贴，多一层爱护！双重保护。\n", + "正向\n", + " 蒙牛是店大欺客 至少态度比蒙牛好～～后期赶紧整改！\n", + "正向\n", + "宝贝收到了，孩子爱吃，敏感肌能用，很启发大脑，提升睡眠质量，五星好评。\n", + "正向\n", + "五星好评，分期付清\n", + "正向\n", + "对得起我们吗？rnm，退钱！\n", + "负向\n" + ] + } + ], + "source": [ + "def score_pred(string):\n", + " words = \" \".join(jieba.cut(string))\n", + " # 预测并输出\n", + " result = 1 if get_score(string) > 0 else 0\n", + " if result == 1:\n", + " print(\"正向\")\n", + " else:\n", + " print(\"负向\")\n", + "\n", + "\n", + "# 使用上述模型进行预测\n", + "test_comment = [\n", + " \"我谢谢蒙牛！多一层体贴，多一层爱护！双重保护。\",\n", + " \" 蒙牛是店大欺客 至少态度比蒙牛好～～后期赶紧整改！\",\n", + " \"宝贝收到了，孩子爱吃，敏感肌能用，很启发大脑，提升睡眠质量，五星好评。\",\n", + " \"五星好评，分期付清\",\n", + " \"对得起我们吗？rnm，退钱！\"\n", + "]\n", + "for comment in test_comment:\n", + " print(comment)\n", + " score_pred(comment)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 感觉 用 这个 手机 下载 MP3 听 很 方便 ， 用 T - Flash 卡装 上 歌 ...\n", + "1 外观 美观 ， 速度 也 不错 。 上面 一排 触摸 键 挺 实用 。 应该 对得起 这个 ...\n", + "2 我刚 拿到 书 ， 也 没有 仔细阅读 ， 就是 粗粗 的 翻 了 点 ， 觉得 还 行 。...\n", + "3 对于 二胡曲 的 精选 ， 应该 出版 一个系列 套装 ， 可 分别 出售 ， 单独 购买 ...\n", + "4 用 了 一年 半 的 e680 终于 送 小偷 了 。 刚刚 买 了 一台 e850...\n", + "Name: raw, dtype: object" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 基于词袋模型进行情感分析\n", + "# 将之前用jieba分好的列表转化为空格分隔的字符串，以方便使用词袋\n", + "df_cleantxt = df_cut.apply(lambda x: \" \".join(x))\n", + "\n", + "df_cleantxt.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<20582x12014 sparse matrix of type ''\n", + "\twith 513919 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countvec = CountVectorizer(min_df = 5) # 特征向量，出现5次以上的才纳入\n", + "# 转化为词频矩阵\n", + "wordmtx = countvec.fit_transform(df_cleantxt)\n", + "\n", + "wordmtx # 一个稀疏矩阵对象，无法直接显示" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LibSVM]" + ] + }, + { + "data": { + "text/plain": [ + "SVC(verbose=True)" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 按照7：3的比例生成训练集和测试集，并使用SVM进行建模\n", + "x_train, x_test, y_train, y_test = train_test_split(wordmtx, df.y, test_size=0.3, random_state=0) # 指定random_state可以确保每次分割出的训练集和测试集相同\n", + "# 创建分类器，使用径向基核函数作为核函数\n", + "clf = SVC(kernel=\"rbf\", verbose=True)\n", + "# 训练分类器（耗时很长，慎点）\n", + "clf.fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "该分类器的准确度为： 0.9551606857777469\n" + ] + } + ], + "source": [ + "# 准确度评估\n", + "print(\"该分类器的准确度为：\", clf.score(x_train, y_train))" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.89 0.88 3047\n", + " 1 0.89 0.87 0.88 3128\n", + "\n", + " accuracy 0.88 6175\n", + " macro avg 0.88 0.88 0.88 6175\n", + "weighted avg 0.88 0.88 0.88 6175\n", + "\n" + ] + } + ], + "source": [ + "# 支持向量机的评估报告\n", + "# 四个列名分别为：精确率、召回率、F1值、支持数量\n", + "# 三个行名分别为：准确率、宏平均、加权平均\n", + "print(classification_report(y_test, clf.predict(x_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "我谢谢蒙牛！多一层体贴，多一层爱护！双重保护。\n", + "正向\n", + " 蒙牛是店大欺客 至少态度比蒙牛好～～后期赶紧整改！\n", + "负向\n", + "宝贝收到了，孩子爱吃，敏感肌能用，很启发大脑，提升睡眠质量，五星好评。\n", + "正向\n", + "五星好评，分期付清\n", + "负向\n", + "对得起我们吗？rnm，退钱！\n", + "负向\n" + ] + } + ], + "source": [ + "def bow_pred(string): \n", + " global clf, countvec\n", + " model = clf\n", + " # 数据转换为词频矩阵\n", + " words = \" \".join(jieba.cut(string))\n", + " words_vecs = countvec.transform([words])\n", + " # 预测并输出\n", + " result = model.predict(words_vecs)\n", + " if result[0] == 1:\n", + " print(\"正向\")\n", + " else:\n", + " print(\"负向\")\n", + "\n", + "\n", + "# 使用上述模型进行预测\n", + "for comment in test_comment:\n", + " print(comment)\n", + " bow_pred(comment)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6274592, 9394570)" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 基于Word2Vec的情感分析\n", + "# 按照7：3的比例生成训练集和测试集\n", + "x_train, x_test, y_train, y_test = train_test_split(df_cut, df.y, test_size=0.3, random_state=0)\n", + "# 初始化word2vec模型和词表\n", + "n_dim = 300 # 指定向量维度\n", + "w2vmodel = Word2Vec(vector_size=n_dim, min_count=10) # 出现10次以上的才纳入\n", + "# 生成词表\n", + "w2vmodel.build_vocab(x_train)\n", + "w2vmodel.train(x_train, total_examples=w2vmodel.corpus_count, epochs = 10) # 训练模型" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 \\\n", + "0 0.287859 0.211170 -0.002694 0.015414 -0.130773 -0.252820 -0.173765 \n", + "1 0.125504 0.210281 0.242841 0.152100 -0.118140 -0.105682 0.000601 \n", + "2 0.208149 0.200255 0.028664 0.096052 0.157904 0.190969 -0.003578 \n", + "3 0.390679 0.235006 0.279163 0.160066 0.273807 -0.093850 -0.012255 \n", + "4 0.226386 0.250651 -0.100598 -0.016630 -0.165644 -0.165641 -0.108314 \n", + "\n", + " 7 8 9 ... 290 291 292 293 \\\n", + "0 0.019247 0.139526 0.084161 ... -0.174484 0.183404 -0.012513 -0.207201 \n", + "1 0.091197 0.157458 0.128822 ... -0.048829 0.089328 -0.048414 -0.100801 \n", + "2 0.351330 -0.182146 0.020400 ... -0.055078 0.228487 0.066214 0.011649 \n", + "3 0.124318 0.116129 0.007561 ... -0.092300 -0.054543 -0.191227 -0.123319 \n", + "4 0.047699 0.030386 0.061525 ... -0.093842 0.121937 0.224285 -0.055072 \n", + "\n", + " 294 295 296 297 298 299 \n", + "0 0.414504 0.289306 0.317440 -0.280071 0.093196 0.119094 \n", + "1 0.153824 0.320274 0.117689 -0.306226 0.321337 -0.032039 \n", + "2 -0.027820 0.206249 -0.050040 -0.125792 0.412140 -0.081710 \n", + "3 0.179981 0.214038 0.072776 -0.226328 0.267805 -0.407456 \n", + "4 0.381097 0.108929 0.158639 0.004664 -0.072203 -0.276887 \n", + "\n", + "[5 rows x 300 columns]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def m_avgvec(words, w2vmodel):\n", + " \"\"\"\n", + " 用各个词向量直接平均的方式生成整句对应的向量\n", + " \"\"\"\n", + " return pd.DataFrame([w2vmodel.wv[w] for w in words if w in w2vmodel.wv]).agg(\"mean\")\n", + "\n", + "\n", + "# 生成建模用矩阵（耗时很长，慎点）\n", + "train_vecs = pd.DataFrame([m_avgvec(s, w2vmodel) for s in x_train])\n", + "\n", + "train_vecs.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LibSVM]" + ] + }, + { + "data": { + "text/plain": [ + "SVC(verbose=True)" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 用转换后的矩阵拟合SVM模型\n", + "clf2 = SVC(kernel=\"rbf\", verbose=True)\n", + "# 训练分类器（耗时很长，慎点）\n", + "clf2.fit(train_vecs, y_train)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "该分类器的准确度为： 0.8936628028041924\n" + ] + } + ], + "source": [ + "# 准确度评估\n", + "print(\"该分类器的准确度为：\", clf2.score(train_vecs, y_train))" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "# 生成测试用矩阵（耗时很长，慎点）\n", + "test_vecs = pd.DataFrame([m_avgvec(s, w2vmodel) for s in x_test])" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.87 0.87 3047\n", + " 1 0.87 0.87 0.87 3128\n", + "\n", + " accuracy 0.87 6175\n", + " macro avg 0.87 0.87 0.87 6175\n", + "weighted avg 0.87 0.87 0.87 6175\n", + "\n" + ] + } + ], + "source": [ + "# 支持向量机的评估报告\n", + "print(classification_report(y_test, clf2.predict(test_vecs)))" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "我谢谢蒙牛！多一层体贴，多一层爱护！双重保护。\n", + "正向\n", + " 蒙牛是店大欺客 至少态度比蒙牛好～～后期赶紧整改！\n", + "负向\n", + "宝贝收到了，孩子爱吃，敏感肌能用，很启发大脑，提升睡眠质量，五星好评。\n", + "正向\n", + "五星好评，分期付清\n", + "正向\n", + "对得起我们吗？rnm，退钱！\n", + "负向\n" + ] + } + ], + "source": [ + "def w2v_pred(string):\n", + " global clf2, w2vmodel\n", + " # 将句子转换为矩阵形式\n", + " vecs = pd.DataFrame([m_avgvec(string, w2vmodel)])\n", + " # 预测并输出\n", + " result = clf2.predict(vecs)\n", + " if result[0] == 1:\n", + " print(\"正向\")\n", + " else:\n", + " print(\"负向\") \n", + "\n", + "\n", + "# 使用上述模型进行预测\n", + "for comment in test_comment:\n", + " print(comment)\n", + " w2v_pred(comment)" + ] + } + ], + "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": "3.7.9" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}