{"id":81219,"date":"2026-07-25T00:34:34","date_gmt":"2026-07-24T16:34:34","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/81219.html"},"modified":"2026-07-25T00:34:34","modified_gmt":"2026-07-24T16:34:34","slug":"02-%e6%9c%b4%e7%b4%a0%e8%b4%9d%e5%8f%b6%e6%96%af-%e4%b9%a6%e7%b1%8d%e8%af%84%e8%ae%ba%e6%83%85%e6%84%9f%e5%88%86%e6%9e%90%e9%a2%84%e6%b5%8b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/81219.html","title":{"rendered":"02-\u6734\u7d20\u8d1d\u53f6\u65af-\u4e66\u7c4d\u8bc4\u8bba\u60c5\u611f\u5206\u6790\u9884\u6d4b"},"content":{"rendered":"

1. \u9700\u6c42\u5206\u6790<\/h2>\n

\u6839\u636e\u8bc4\u8bba\u8bed，\u5224\u65ad\u8bc4\u8bba\u662f\u597d\u8bc4\u8fd8\u662f\u5dee\u8bc4<\/p>\n

2. \u6570\u636e\u8bf4\u660e<\/h2>\n

\u4e66\u7c4d\u8bc4\u4ef7\u6570\u636e：<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u5185\u5bb9<\/span><\/td>\n\u8bc4\u4ef7<\/span><\/td>\n<\/tr>\n
\u00a0\u4ece\u7f16\u7a0b\u5c0f\u767d\u7684\u89d2\u5ea6\u770b，\u5165\u95e8\u6781\u4f73\u3002<\/span><\/td>\n\u597d\u8bc4<\/span><\/td>\n<\/tr>\n
\u5f88\u597d\u7684\u5165\u95e8\u4e66，\u7b80\u6d01\u5168\u9762，\u9002\u5408\u5c0f\u767d\u3002<\/span><\/td>\n\u597d\u8bc4<\/span><\/td>\n<\/tr>\n
\u8bb2\u89e3\u5168\u9762，\u8bb8\u591a\u5c0f\u7ec6\u8282\u90fd\u6709\u987e\u53ca，\u4e09\u4e2a\u5c0f\u9879\u76ee\u53d7\u76ca\u532a\u6d45\u3002<\/span><\/td>\n\u597d\u8bc4<\/span><\/td>\n<\/tr>\n
\u524d\u534a\u90e8\u5206\u8bb2\u6982\u5ff5\u6df1\u5165\u6d45\u51fa，\u8981\u8a00\u4e0d\u70e6，\u5f88\u8d5e<\/span><\/td>\n\u597d\u8bc4<\/span><\/td>\n<\/tr>\n
\u770b\u4e86\u4e00\u904d\u8fd8\u662f\u4e0d\u4f1a\u5199，\u6709\u4e2a\u6982\u5ff5\u800c\u5df2<\/span><\/td>\n\u5dee\u8bc4<\/span><\/td>\n<\/tr>\n
\u4e2d\u89c4\u4e2d\u77e9\u7684\u6559\u79d1\u4e66，\u96f6\u57fa\u7840\u7684\u770b\u4e86\u4f9d\u65e7\u770b\u4e0d\u61c2<\/span><\/td>\n\u5dee\u8bc4<\/span><\/td>\n<\/tr>\n
\u5185\u5bb9\u592a\u6d45\u663e，\u4e2a\u4eba\u8ba4\u4e3a\u4e0d\u9002\u5408\u6709\u5176\u5b83\u8bed\u8a00\u7f16\u7a0b\u57fa\u7840\u7684\u4eba<\/span><\/td>\n\u5dee\u8bc4<\/span><\/td>\n<\/tr>\n
\u7834\u4e66\u4e00\u672c<\/span><\/td>\n\u5dee\u8bc4<\/span><\/td>\n<\/tr>\n
\u9002\u5408\u5b8c\u5b8c\u5168\u5168\u7684\u5c0f\u767d\u8bfb，\u6709\u5176\u4ed6\u8bed\u8a00\u7ecf\u9a8c\u7684\u53ef\u4ee5\u53bb\u770b\u522b\u7684\u4e66<\/span><\/td>\n\u5dee\u8bc4<\/span><\/td>\n<\/tr>\n
\u57fa\u7840\u77e5\u8bc6\u5199\u7684\u633a\u597d\u7684！<\/span><\/td>\n\u597d\u8bc4<\/span><\/td>\n<\/tr>\n
\u592a\u57fa\u7840<\/span><\/td>\n\u5dee\u8bc4<\/span><\/td>\n<\/tr>\n
\u7565_\u55e6\u3002\u3002\u9002\u5408\u5b8c\u5168\u6ca1\u6709\u7f16\u7a0b\u7ecf\u9a8c\u7684\u5c0f\u767d<\/span><\/td>\n\u5dee\u8bc4<\/span><\/td>\n<\/tr>\n
\u771f\u7684\u771f\u7684\u4e0d\u5efa\u8bae\u4e70<\/span><\/td>\n\u5dee\u8bc4<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u505c\u7528\u8bcd\u6570\u636e：<\/p>\n<\/p>\n

3. \u5efa\u6a21<\/h2>\n

import jieba
\nimport pandas as pd
\nfrom matplotlib import pyplot as plt
\nfrom sklearn.feature_extraction.text import CountVectorizer
\nfrom sklearn.metrics import classification_report, roc_curve, auc
\nfrom sklearn.model_selection import train_test_split
\nfrom sklearn.naive_bayes import MultinomialNB<\/p>\n

3.1 \u52a0\u8f7d\u6570\u636e<\/h3>\n

# 1. \u52a0\u8f7d\u4e66\u7c4d
\n# 1.1 \u4e66\u7c4d\u8bc4\u4ef7\u6570\u636e
\ndata = pd.read_csv('.\/data\/\u4e66\u7c4d\u8bc4\u4ef7.csv', encoding='gbk', index_col=0)
\ndata.head()<\/p>\n

# 1.2 \u505c\u7528\u8bcd\u5217\u8868
\nwith open('.\/data\/stopwords.txt', 'r', encoding='utf-8') as f:
\n stop_words_list = f.readlines() # \u8bfb\u53d6\u6240\u6709\u884c
\n stop_words_list = [line.strip() for line in stop_words_list] # \u53bb\u9664\u6362\u884c\u7b26
\n stop_words_list = list(set(stop_words_list)) # \u53bb\u91cd<\/p>\n

stop_words_list[:10]<\/p>\n

3.2 \u6570\u636e\u9884\u5904\u7406<\/h3>\n

# 2. \u6570\u636e\u9884\u5904\u7406
\n# 2.1 \u8bc4\u4ef7\u6807\u7b7e\u8f6c\u6362
\ndata['label'] = data['\u8bc4\u4ef7'].map({'\u597d\u8bc4': 1, '\u5dee\u8bc4': 0})
\ny = data['label']<\/p>\n

# 2.2 jieba\u5206\u8bcd
\ncomment_list = [','.join(jieba.lcut(line)) for line in data['\u5185\u5bb9'].values]
\ncomment_list[:5]<\/p>\n

# 2.3 \u5254\u9664\u505c\u7528\u8bcd
\ntransfer = CountVectorizer(stop_words=stop_words_list)
\nx = transfer.fit_transform(comment_list).toarray() # \u6bcf\u6761\u8bc4\u8bba\u7684\u5207\u8bcd\u5206\u5e03，\u6709\u5c31\u662f1，\u6ca1\u6709\u5c31\u662f0，\u6bcf\u6761\u8f6c\u6362\u540e\u4ee5\u5217\u8868\u5f62\u5f0f\u4fdd\u5b58
\nprint(x[0])
\nprint(len(transfer.get_feature_names_out())) # \u89e3\u91ca：\u8bcd\u888b\u6a21\u578b\u4e2d，\u6700\u7ec8\u6709\u591a\u5c11\u4e2a\u8bcd<\/p>\n

# 2.4 \u5212\u5206\u6570\u636e\u96c6
\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0, stratify=y)
\n# len(x_train)<\/p>\n

3.3 \u7279\u5f81\u5de5\u7a0b（\u4e0d\u9700\u8981）<\/h3>\n

3.4 \u6a21\u578b\u8bad\u7ec3<\/h3>\n

# 4. \u6a21\u578b\u8bad\u7ec3
\nestimator = MultinomialNB() # \u591a\u9879\u5f0f\u8d1d\u53f6\u65af
\nestimator.fit(x_train, y_train)
\ny_pre = estimator.predict(x_test)<\/p>\n

3.5 \u6a21\u578b\u8bc4\u4f30<\/h3>\n

# 5. \u6a21\u578b\u8bc4\u4f30
\nprint('\u5206\u7c7b\u62a5\u544a:', classification_report(y_test, y_pre))<\/p>\n

# \u53ef\u89c6\u5316
\nfpr, tpr, thresholds = roc_curve(y_test, estimator.predict_proba(x_test)[:,1])
\nauc_value = auc(fpr, tpr)
\nplt.figure(figsize=(3,3))
\nplt.plot(fpr, tpr, label='AUC = %.2f' % auc_value)
\nplt.plot([0,1], [0,1], 'r–')
\nplt.xlabel('False Positive Rate')
\nplt.ylabel('True Positive Rate')
\nplt.legend()
\nplt.show()<\/p>\n","protected":false},"excerpt":{"rendered":"

1. \u9700\u6c42\u5206\u6790\u6839\u636e\u8bc4\u8bba\u8bed，\u5224\u65ad\u8bc4\u8bba\u662f\u597d\u8bc4\u8fd8\u662f\u5dee\u8bc42. \u6570\u636e\u8bf4\u660e\u4e66\u7c4d\u8bc4\u4ef7\u6570\u636e：\u5185\u5bb9\u8bc4\u4ef7\u00a0\u4ece\u7f16\u7a0b\u5c0f\u767d\u7684\u89d2\u5ea6\u770b，\u5165\u95e8\u6781\u4f73\u3002\u597d\u8bc4\u5f88\u597d\u7684\u5165\u95e8\u4e66，\u7b80\u6d01\u5168\u9762，\u9002\u5408\u5c0f\u767d\u3002\u597d\u8bc4\u8bb2\u89e3\u5168\u9762，\u8bb8\u591a\u5c0f\u7ec6\u8282\u90fd\u6709\u987e\u53ca，\u4e09\u4e2a\u5c0f\u9879\u76ee\u53d7\u76ca\u532a\u6d45\u3002\u597d\u8bc4\u524d\u534a\u90e8\u5206\u8bb2\u6982\u5ff5\u6df1\u5165\u6d45\u51fa，\u8981\u8a00\u4e0d\u70e6，\u5f88\u8d5e\u597d\u8bc4\u770b\u4e86\u4e00\u904d\u8fd8\u662f\u4e0d\u4f1a\u5199，\u6709\u4e2a\u6982\u5ff5\u800c\u5df2\u5dee\u8bc4\u4e2d\u89c4\u4e2d\u77e9\u7684\u6559\u79d1\u4e66，\u96f6\u57fa\u7840\u7684\u770b\u4e86\u4f9d\u65e7\u770b<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,207],"topic":[],"class_list":["post-81219","post","type-post","status-publish","format-standard","hentry","category-server","tag-50","tag-207"],"yoast_head":"\n02-\u6734\u7d20\u8d1d\u53f6\u65af-\u4e66\u7c4d\u8bc4\u8bba\u60c5\u611f\u5206\u6790\u9884\u6d4b - 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