{"id":1000602,"date":"2024-12-27T09:51:36","date_gmt":"2024-12-27T01:51:36","guid":{"rendered":""},"modified":"2024-12-27T09:51:41","modified_gmt":"2024-12-27T01:51:41","slug":"python%e5%a6%82%e4%bd%95%e8%ae%ad%e7%bb%83%e6%83%85%e6%84%9f%e8%af%8d%e5%85%b8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1000602.html","title":{"rendered":"python\u5982\u4f55\u8bad\u7ec3\u60c5\u611f\u8bcd\u5178"},"content":{"rendered":"

\"python\u5982\u4f55\u8bad\u7ec3\u60c5\u611f\u8bcd\u5178\"<\/p>\n

Python\u8bad\u7ec3\u60c5\u611f\u8bcd\u5178\u53ef\u4ee5\u901a\u8fc7\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5de5\u5177\u5305\u3001\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u3001\u4eba\u5de5\u6807\u6ce8\u7684\u6570\u636e\u96c6\u6765\u5b9e\u73b0<\/strong>\uff0c\u5176\u4e2d\u4f7f\u7528\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5de5\u5177\u5305\u662f\u6700\u57fa\u7840\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u60a8\u53ef\u4ee5\u5bf9\u6587\u672c\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3001\u7279\u5f81\u63d0\u53d6\u4ee5\u53ca\u60c5\u611f\u5206\u6790\uff0c\u6700\u7ec8\u751f\u6210\u4e00\u4e2a\u60c5\u611f\u8bcd\u5178\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u8bad\u7ec3\u60c5\u611f\u8bcd\u5178\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5de5\u5177\u5305<\/p>\n<\/p>\n

\u4f7f\u7528\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5de5\u5177\u5305\uff08\u5982NLTK\u3001TextBlob\u3001spaCy\u7b49\uff09\u662f\u8bad\u7ec3\u60c5\u611f\u8bcd\u5178\u7684\u57fa\u7840\u65b9\u6cd5\u3002\u8fd9\u4e9b\u5de5\u5177\u5305\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6587\u672c\u5904\u7406\u529f\u80fd\u548c\u9884\u8bad\u7ec3\u7684\u60c5\u611f\u5206\u6790\u6a21\u578b\u3002<\/p>\n<\/p>\n

    \n
  1. NLTK<\/li>\n<\/ol>\n

    NLTK\uff08Natural Language Toolkit\uff09\u662f\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684Python\u5e93\uff0c\u4e13\u4e3a\u5904\u7406\u4eba\u7c7b\u8bed\u8a00\u6570\u636e\u800c\u8bbe\u8ba1\u3002NLTK\u5305\u542b\u8bb8\u591a\u8bed\u6599\u5e93\u548c\u8bcd\u6c47\u8d44\u6e90\uff0c\u5e76\u6709\u4e30\u5bcc\u7684\u6587\u672c\u5904\u7406\u5de5\u5177\u3002<\/p>\n<\/p>\n

    \u901a\u8fc7NLTK\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u73b0\u6709\u7684\u60c5\u611f\u5206\u6790\u529f\u80fd\u6765\u5206\u6790\u6587\u672c\u60c5\u611f\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528VADER\u60c5\u611f\u5206\u6790\u5668\uff0c\u8fd9\u662f\u4e00\u4e2a\u57fa\u4e8e\u8bcd\u5178\u7684\u65b9\u6cd5\uff0c\u4e13\u4e3a\u793e\u4ea4\u5a92\u4f53\u6587\u672c\u8bbe\u8ba1\u3002<\/p>\n<\/p>\n

    import nltk<\/p>\n
    from nltk.sentiment.vader import SentimentIntensityAnalyzer<\/p>\n
    nltk.download('vader_lexicon')<\/p>\n
    sid = SentimentIntensityAnalyzer()<\/p>\n
    text = "This is a fantastic movie!"<\/p>\n
    sentiment_scores = sid.polarity_scores(text)<\/p>\n
    print(sentiment_scores)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. TextBlob<\/li>\n<\/ol>\n
      TextBlob\u662f\u4e00\u4e2a\u7b80\u5355\u6613\u7528\u7684Python\u5e93\uff0c\u63d0\u4f9b\u57fa\u672c\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\uff0c\u5305\u62ec\u60c5\u611f\u5206\u6790\u3002TextBlob\u7684\u60c5\u611f\u5206\u6790\u529f\u80fd\u662f\u57fa\u4e8e\u6a21\u5f0f\u5e93\u7684\u3002<\/p>\n<\/p>\n
      from textblob import TextBlob<\/p>\n
      text = "I love this beautiful place!"<\/p>\n
      blob = TextBlob(text)<\/p>\n
      print(blob.sentiment)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. spaCy<\/li>\n<\/ol>\n
        spaCy\u662f\u4e00\u4e2a\u9ad8\u7ea7\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\uff0c\u4e13\u6ce8\u4e8e\u901f\u5ea6\u548c\u6548\u7387\u3002\u867d\u7136spaCy\u672c\u8eab\u4e0d\u63d0\u4f9b\u60c5\u611f\u5206\u6790\u529f\u80fd\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u6269\u5c55\u5e93\uff08\u5982TextBlob\u6216\u5176\u4ed6\uff09\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
        import spacy<\/p>\n
        from textblob import TextBlob<\/p>\n
        nlp = spacy.load("en_core_web_sm")<\/p>\n
        text = "The product is good but the service was terrible."<\/p>\n
        doc = nlp(text)<\/p>\n
        Use TextBlob for sentiment analysis<\/strong><\/h2>\n
        blob = TextBlob(doc.text)<\/p>\n
        print(blob.sentiment)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e8c\u3001\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/p>\n<\/p>\n
        \u9664\u4e86\u76f4\u63a5\u4f7f\u7528\u5de5\u5177\u5305\uff0c\u60a8\u8fd8\u53ef\u4ee5\u901a\u8fc7\u8bad\u7ec3\u673a\u5668\u5b66\u4e60\u6a21\u578b\u6765\u6784\u5efa\u60c5\u611f\u8bcd\u5178\u3002\u8fd9\u79cd\u65b9\u6cd5\u901a\u5e38\u9700\u8981\u4e00\u4e2a\u6807\u6ce8\u597d\u7684\u60c5\u611f\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
          \n
        1. \u6570\u636e\u96c6\u51c6\u5907<\/li>\n<\/ol>\n
          \u9996\u5148\uff0c\u60a8\u9700\u8981\u4e00\u4e2a\u6807\u6ce8\u597d\u7684\u60c5\u611f\u6570\u636e\u96c6\u3002\u5e38\u7528\u7684\u6570\u636e\u96c6\u5305\u62ecIMDb\u7535\u5f71\u8bc4\u8bba\u6570\u636e\u96c6\u3001Twitter\u60c5\u611f\u6570\u636e\u96c6\u7b49\u3002\u8fd9\u4e9b\u6570\u636e\u96c6\u901a\u5e38\u5305\u542b\u6587\u672c\u548c\u76f8\u5e94\u7684\u60c5\u611f\u6807\u7b7e\uff08\u5982\u6b63\u9762\u3001\u8d1f\u9762\uff09\u3002<\/p>\n<\/p>\n
            \n
          1. \u6570\u636e\u9884\u5904\u7406<\/li>\n<\/ol>\n
            \u5728\u8bad\u7ec3\u6a21\u578b\u4e4b\u524d\uff0c\u60a8\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002\u8fd9\u5305\u62ec\u53bb\u9664\u505c\u7528\u8bcd\u3001\u6807\u70b9\u7b26\u53f7\u3001\u5c0f\u5199\u5316\u7b49\u3002\u6b64\u5916\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u5c06\u6587\u672c\u8f6c\u5316\u4e3a\u5411\u91cf\u5f62\u5f0f\uff08\u5982TF-IDF\u3001\u8bcd\u5d4c\u5165\uff09\u3002<\/p>\n<\/p>\n
            from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
            from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
            \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u6570\u636e\u96c6df\uff0c\u5176\u4e2d\u5305\u542b"review"\u548c"sentiment"\u5217<\/strong><\/h2>\n
            X = df['review']<\/p>\n
            y = df['sentiment']<\/p>\n
            \u5c06\u6587\u672c\u6570\u636e\u8f6c\u6362\u4e3aTF-IDF\u7279\u5f81<\/strong><\/h2>\n
            vectorizer = TfidfVectorizer(max_features=5000)<\/p>\n
            X_tfidf = vectorizer.fit_transform(X)<\/p>\n
            \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
            X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.2, random_state=42)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u6a21\u578b\u8bad\u7ec3<\/li>\n<\/ol>\n
              \u63a5\u4e0b\u6765\uff0c\u60a8\u53ef\u4ee5\u9009\u62e9\u4e00\u4e2a\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u6765\u8bad\u7ec3\u6a21\u578b\u3002\u5e38\u7528\u7684\u7b97\u6cd5\u5305\u62ec\u903b\u8f91\u56de\u5f52\u3001\u652f\u6301\u5411\u91cf\u673a\u3001\u968f\u673a\u68ee\u6797\u7b49\u3002<\/p>\n<\/p>\n
              from sklearn.linear_model import LogisticRegression<\/p>\n
              from sklearn.metrics import classification_report, accuracy_score<\/p>\n
              \u8bad\u7ec3\u903b\u8f91\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
              model = LogisticRegression()<\/p>\n
              model.fit(X_train, y_train)<\/p>\n
              \u9884\u6d4b\u5e76\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
              y_pred = model.predict(X_test)<\/p>\n
              print(classification_report(y_test, y_pred))<\/p>\n
              print("Accuracy:", accuracy_score(y_test, y_pred))<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u63d0\u53d6\u60c5\u611f\u8bcd\u5178<\/li>\n<\/ol>\n
                \u4e00\u65e6\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\uff0c\u60a8\u53ef\u4ee5\u5206\u6790\u6a21\u578b\u7684\u7279\u5f81\u91cd\u8981\u6027\u6216\u7cfb\u6570\uff0c\u4ee5\u63d0\u53d6\u51fa\u60c5\u611f\u8bcd\u5178\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff0c\u60a8\u53ef\u4ee5\u67e5\u770b\u6bcf\u4e2a\u7279\u5f81\uff08\u8bcd\uff09\u7684\u6743\u91cd\u3002<\/p>\n<\/p>\n
                feature_names = vectorizer.get_feature_names_out()<\/p>\n
                coefficients = model.coef_[0]<\/p>\n
                \u5c06\u7279\u5f81\u53ca\u5176\u5bf9\u5e94\u7684\u7cfb\u6570\u7ec4\u5408\u6210\u4e00\u4e2a\u8bcd\u5178<\/strong><\/h2>\n
                sentiment_dict = dict(zip(feature_names, coefficients))<\/p>\n
                \u6253\u5370\u6700\u5177\u6b63\u9762\u548c\u8d1f\u9762\u5f71\u54cd\u7684\u8bcd<\/strong><\/h2>\n
                print("Top positive words:", sorted(sentiment_dict.items(), key=lambda x: x[1], reverse=True)[:10])<\/p>\n
                print("Top negative words:", sorted(sentiment_dict.items(), key=lambda x: x[1])[:10])<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u4e09\u3001\u4eba\u5de5\u6807\u6ce8\u7684\u6570\u636e\u96c6<\/p>\n<\/p>\n
                \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684\u60c5\u611f\u8bcd\u5178\u3002\u8fd9\u9700\u8981\u4eba\u5de5\u6807\u6ce8\u7684\u6570\u636e\u96c6\uff0c\u5e76\u7ed3\u5408\u4e13\u5bb6\u77e5\u8bc6\u3002<\/p>\n<\/p>\n
                  \n
                1. \u6570\u636e\u6807\u6ce8<\/li>\n<\/ol>\n
                  \u624b\u52a8\u6807\u6ce8\u4e00\u90e8\u5206\u6570\u636e\u96c6\uff0c\u4ee5\u786e\u5b9a\u6587\u672c\u4e2d\u7684\u60c5\u611f\u8bcd\u53ca\u5176\u5bf9\u5e94\u7684\u60c5\u611f\u503c\u3002\u8fd9\u662f\u4e00\u9879\u8017\u65f6\u4e14\u9700\u8981\u8bed\u8a00\u77e5\u8bc6\u7684\u5de5\u4f5c\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u8bcd\u5178\u6784\u5efa<\/li>\n<\/ol>\n
                    \u6839\u636e\u6807\u6ce8\u6570\u636e\uff0c\u6784\u5efa\u521d\u59cb\u60c5\u611f\u8bcd\u5178\u3002\u8fd9\u4e2a\u8bcd\u5178\u53ef\u4ee5\u7528\u4f5c\u540e\u7eed\u6a21\u578b\u8bad\u7ec3\u7684\u57fa\u7840\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u6a21\u578b\u5fae\u8c03<\/li>\n<\/ol>\n
                      \u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b\u5bf9\u60c5\u611f\u8bcd\u5178\u8fdb\u884c\u5fae\u8c03\uff0c\u4ee5\u63d0\u9ad8\u5176\u51c6\u786e\u6027\u548c\u9002\u7528\u6027\u3002\u8fd9\u53ef\u80fd\u5305\u62ec\u5bf9\u6a21\u578b\u8fdb\u884c\u518d\u8bad\u7ec3\uff0c\u7ed3\u5408\u65b0\u7684\u6807\u6ce8\u6570\u636e\u548c\u81ea\u52a8\u751f\u6210\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u8bc4\u4f30\u548c\u6539\u8fdb<\/li>\n<\/ol>\n
                        \u5b9a\u671f\u8bc4\u4f30\u60c5\u611f\u8bcd\u5178\u7684\u6027\u80fd\uff0c\u5e76\u6839\u636e\u53cd\u9988\u548c\u65b0\u6570\u636e\u8fdb\u884c\u6539\u8fdb\u3002\u8fd9\u9700\u8981\u6301\u7eed\u7684\u7ef4\u62a4\u548c\u66f4\u65b0\uff0c\u4ee5\u786e\u4fdd\u8bcd\u5178\u7684\u51c6\u786e\u6027\u548c\u5b9e\u7528\u6027\u3002<\/p>\n<\/p>\n
                        \u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u6709\u6548\u5730\u8bad\u7ec3\u60c5\u611f\u8bcd\u5178\uff0c\u4ee5\u4fbf\u5728\u5404\u79cd\u5e94\u7528\u573a\u666f\u4e2d\u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002\u65e0\u8bba\u662f\u4f7f\u7528\u73b0\u6709\u7684\u5de5\u5177\u5305\u3001\u8bad\u7ec3\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u8fd8\u662f\u6784\u5efa\u81ea\u5b9a\u4e49\u8bcd\u5178\uff0c\u90fd\u9700\u8981\u7ed3\u5408\u7279\u5b9a\u7684\u5e94\u7528\u9700\u6c42\u548c\u6570\u636e\u7279\u70b9\u8fdb\u884c\u8c03\u6574\u548c\u4f18\u5316\u3002<\/p>\n<\/p>\n
                        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                         \u5982\u4f55\u6784\u5efa\u4e00\u4e2a\u60c5\u611f\u8bcd\u5178\uff1f<\/strong>
                        \u6784\u5efa\u60c5\u611f\u8bcd\u5178\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\u6536\u96c6\u60c5\u611f\u8bcd\u6c47\u3001\u6807\u6ce8\u6bcf\u4e2a\u8bcd\u6c47\u7684\u60c5\u611f\u503e\u5411\u3001\u5e76\u5c06\u5176\u5b58\u50a8\u4e3a\u6613\u4e8e\u8bbf\u95ee\u7684\u683c\u5f0f\u3002\u53ef\u4ee5\u5229\u7528\u73b0\u6709\u7684\u60c5\u611f\u8bcd\u5178\u4f5c\u4e3a\u53c2\u8003\uff0c\u5e76\u7ed3\u5408\u673a\u5668\u5b66\u4e60\u6280\u672f\uff0c\u4ece\u793e\u4ea4\u5a92\u4f53\u3001\u8bc4\u8bba\u548c\u5176\u4ed6\u6587\u672c\u6570\u636e\u4e2d\u63d0\u53d6\u60c5\u611f\u76f8\u5173\u7684\u8bcd\u6c47\u3002<\/p>\n
                        \u60c5\u611f\u8bcd\u5178\u7684\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u9700\u8981\u54ea\u4e9b\u6570\u636e\uff1f<\/strong>
                        \u5728\u8bad\u7ec3\u60c5\u611f\u8bcd\u5178\u65f6\uff0c\u901a\u5e38\u9700\u8981\u6807\u6ce8\u597d\u7684\u6587\u672c\u6570\u636e\uff0c\u8fd9\u4e9b\u6570\u636e\u5e94\u5305\u542b\u591a\u79cd\u60c5\u611f\u8868\u8fbe\uff0c\u5982\u79ef\u6781\u3001\u6d88\u6781\u548c\u4e2d\u6027\u3002\u53ef\u4ee5\u9009\u62e9\u793e\u4ea4\u5a92\u4f53\u8bc4\u8bba\u3001\u4ea7\u54c1\u8bc4\u4ef7\u6216\u65b0\u95fb\u6587\u7ae0\u7b49\u591a\u79cd\u6587\u672c\u6765\u6e90\u6765\u4e30\u5bcc\u6570\u636e\u96c6\uff0c\u4ece\u800c\u63d0\u9ad8\u60c5\u611f\u8bcd\u5178\u7684\u51c6\u786e\u6027\u3002<\/p>\n
                        \u4f7f\u7528Python\u8bad\u7ec3\u60c5\u611f\u8bcd\u5178\u65f6\u6709\u54ea\u4e9b\u63a8\u8350\u7684\u5e93\uff1f<\/strong>
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