{"id":1182096,"date":"2025-01-15T18:59:14","date_gmt":"2025-01-15T10:59:14","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1182096.html"},"modified":"2025-01-15T18:59:16","modified_gmt":"2025-01-15T10:59:16","slug":"%e5%a6%82%e4%bd%95%e6%8a%bd%e5%8f%96%e5%85%b3%e9%94%ae%e5%ad%97python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1182096.html","title":{"rendered":"\u5982\u4f55\u62bd\u53d6\u5173\u952e\u5b57python"},"content":{"rendered":"

\"\u5982\u4f55\u62bd\u53d6\u5173\u952e\u5b57python\"<\/p>\n

\u5982\u4f55\u62bd\u53d6\u5173\u952e\u5b57python\uff1a\u4f7f\u7528TF-IDF\u7b97\u6cd5\u3001\u4f7f\u7528TextRank\u7b97\u6cd5\u3001\u4f7f\u7528\u5206\u8bcd\u5de5\u5177\u3001\u4f7f\u7528\u4e3b\u9898\u6a21\u578b\u3001\u4f7f\u7528\u673a\u5668\u5b66\u4e60<\/a><\/strong><\/p>\n<\/p>\n

\u5728\u672c\u6587\u7684\u5f00\u5934\uff0c\u6211\u4eec\u5c06\u76f4\u63a5\u56de\u7b54\u6807\u9898\u6240\u63d0\u95ee\u9898\u3002\u5173\u952e\u5b57\u62bd\u53d6\u5728\u6587\u672c\u5206\u6790\u4e2d\u662f\u4e00\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u6b65\u9aa4\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u3002\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528TF-IDF\u7b97\u6cd5\u3001\u4f7f\u7528TextRank\u7b97\u6cd5\u3001\u4f7f\u7528\u5206\u8bcd\u5de5\u5177\u3001\u4f7f\u7528\u4e3b\u9898\u6a21\u578b\u3001\u4f7f\u7528\u673a\u5668\u5b66\u4e60<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528TF-IDF\u7b97\u6cd5<\/strong>\u662f\u4e00\u79cd\u5e38\u89c1\u4e14\u6709\u6548\u7684\u5173\u952e\u5b57\u62bd\u53d6\u65b9\u6cd5\u3002TF-IDF\u7b97\u6cd5\u901a\u8fc7\u8ba1\u7b97\u8bcd\u9891\u548c\u9006\u6587\u6863\u9891\u7387\uff0c\u8861\u91cf\u4e00\u4e2a\u8bcd\u5728\u6587\u6863\u4e2d\u7684\u91cd\u8981\u6027\u3002\u5177\u4f53\u6765\u8bf4\uff0cTF-IDF\u503c\u8d8a\u9ad8\uff0c\u8868\u793a\u8be5\u8bcd\u5728\u6587\u6863\u4e2d\u8d8a\u91cd\u8981\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u8fd9\u4e9b\u5173\u952e\u5b57\u62bd\u53d6\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528TF-IDF\u7b97\u6cd5<\/strong><\/h2>\n

TF-IDF\uff08Term Frequency-Inverse Document Frequency\uff09\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6587\u672c\u5206\u6790\u6280\u672f\uff0c\u7528\u4e8e\u8861\u91cf\u4e00\u4e2a\u8bcd\u5728\u6587\u6863\u4e2d\u7684\u91cd\u8981\u6027\u3002\u5b83\u7ed3\u5408\u4e86\u8bcd\u9891\uff08TF\uff09\u548c\u9006\u6587\u6863\u9891\u7387\uff08IDF\uff09\u4e24\u4e2a\u6307\u6807\uff0c\u6765\u8bc4\u4ef7\u4e00\u4e2a\u8bcd\u5728\u6587\u6863\u96c6\u4e2d\u7684\u4ee3\u8868\u6027\u3002TF-IDF\u7b97\u6cd5\u7684\u516c\u5f0f\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

[ \\text{TF-IDF}(t,d) = \\text{TF}(t,d) \\times \\text{IDF}(t) ]<\/p>\n<\/p>\n

\u5176\u4e2d\uff1a<\/p>\n<\/p>\n

    \n
  • (\\text{TF}(t,d)) \u662f\u8bcd (t) \u5728\u6587\u6863 (d) \u4e2d\u51fa\u73b0\u7684\u6b21\u6570\u3002<\/li>\n
  • (\\text{IDF}(t)) \u662f\u8bcd (t) \u5728\u6587\u6863\u96c6\u4e2d\u7684\u9006\u6587\u6863\u9891\u7387\uff0c\u8ba1\u7b97\u516c\u5f0f\u4e3a\uff1a[ \\text{IDF}(t) = \\log \\left( \\frac{N}{1 + \\text{DF}(t)} \\right) ]\uff0c\u5176\u4e2d (N) \u662f\u6587\u6863\u603b\u6570\uff0c(\\text{DF}(t)) \u662f\u5305\u542b\u8bcd (t) \u7684\u6587\u6863\u6570\u91cf\u3002<\/li>\n<\/ul>\n

    1.1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h2>\n<\/p>\n

    from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
    import pandas as pd<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.2\u3001\u51c6\u5907\u6587\u672c\u6570\u636e<\/h2>\n<\/p>\n
    documents = [<\/p>\n
        "Python is a high-level programming language.",<\/p>\n
        "Machine learning and data science are applications of Python.",<\/p>\n
        "Python is popular for web development.",<\/p>\n
        "Data analysis and machine learning are key applications of Python."<\/p>\n
    ]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.3\u3001\u8ba1\u7b97TF-IDF\u503c<\/h2>\n<\/p>\n
    vectorizer = TfidfVectorizer()<\/p>\n
    tfidf_matrix = vectorizer.fit_transform(documents)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.4\u3001\u63d0\u53d6\u5173\u952e\u5b57<\/h2>\n<\/p>\n
    feature_names = vectorizer.get_feature_names_out()<\/p>\n
    for doc in range(len(documents)):<\/p>\n
        df = pd.DataFrame(tfidf_matrix[doc].T.todense(), index=feature_names, columns=["TF-IDF"])<\/p>\n
        df = df.sort_values(by=["TF-IDF"], ascending=False)<\/p>\n
        print(f"Document {doc+1} top keywords:\\n", df.head(5))<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u4f7f\u7528TextRank\u7b97\u6cd5<\/strong><\/h2>\n
    TextRank\u662f\u4e00\u79cd\u57fa\u4e8e\u56fe\u7684\u6392\u5e8f\u7b97\u6cd5\uff0c\u7528\u4e8e\u62bd\u53d6\u6587\u672c\u4e2d\u7684\u91cd\u8981\u4fe1\u606f\u3002\u5b83\u7c7b\u4f3c\u4e8ePageRank\u7b97\u6cd5\uff0c\u6700\u521d\u7528\u4e8e\u7f51\u9875\u6392\u540d\u3002TextRank\u901a\u8fc7\u6784\u5efa\u8bcd\u8bed\u4e4b\u95f4\u7684\u5173\u7cfb\u56fe\uff0c\u5229\u7528\u56fe\u7684\u7ed3\u6784\u6765\u786e\u5b9a\u6bcf\u4e2a\u8bcd\u7684\u91cd\u8981\u6027\u3002<\/p>\n<\/p>\n
    2.1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h2>\n<\/p>\n
    import jieba.analyse<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2.2\u3001\u51c6\u5907\u6587\u672c\u6570\u636e<\/h2>\n<\/p>\n
    text = "Python is a high-level programming language. Machine learning and data science are applications of Python. Python is popular for web development. Data analysis and machine learning are key applications of Python."<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2.3\u3001\u4f7f\u7528TextRank\u7b97\u6cd5\u62bd\u53d6\u5173\u952e\u5b57<\/h2>\n<\/p>\n
    keywords = jieba.analyse.textrank(text, topK=5, withWeight=True)<\/p>\n
    print("Top keywords using TextRank:\\n", keywords)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u4f7f\u7528\u5206\u8bcd\u5de5\u5177<\/strong><\/h2>\n
    \u5206\u8bcd\u662f\u6587\u672c\u5904\u7406\u4e2d\u7684\u57fa\u7840\u6b65\u9aa4\uff0c\u901a\u8fc7\u5c06\u6587\u672c\u5207\u5206\u6210\u4e00\u4e2a\u4e2a\u7684\u8bcd\u8bed\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u8fdb\u884c\u540e\u7eed\u7684\u6587\u672c\u5206\u6790\u3002\u5728Python\u4e2d\uff0c\u6709\u8bb8\u591a\u5206\u8bcd\u5de5\u5177\u53ef\u4f9b\u4f7f\u7528\uff0c\u5982Jieba\u3001NLTK\u3001SpaCy\u7b49\u3002<\/p>\n<\/p>\n
    3.1\u3001\u4f7f\u7528Jieba\u5206\u8bcd<\/h2>\n<\/p>\n
    3.1.1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n
    import jieba<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.1.2\u3001\u51c6\u5907\u6587\u672c\u6570\u636e<\/h3>\n<\/p>\n
    text = "Python is a high-level programming language. Machine learning and data science are applications of Python."<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.1.3\u3001\u8fdb\u884c\u5206\u8bcd<\/h3>\n<\/p>\n
    words = jieba.cut(text)<\/p>\n
    print("Words using Jieba:\\n", "\/".join(words))<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.2\u3001\u4f7f\u7528NLTK\u5206\u8bcd<\/h2>\n<\/p>\n
    3.2.1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n
    import nltk<\/p>\n
    nltk.download('punkt')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.2.2\u3001\u51c6\u5907\u6587\u672c\u6570\u636e<\/h3>\n<\/p>\n
    text = "Python is a high-level programming language. Machine learning and data science are applications of Python."<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.2.3\u3001\u8fdb\u884c\u5206\u8bcd<\/h3>\n<\/p>\n
    words = nltk.word_tokenize(text)<\/p>\n
    print("Words using NLTK:\\n", words)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u4f7f\u7528\u4e3b\u9898\u6a21\u578b<\/strong><\/h2>\n
    \u4e3b\u9898\u6a21\u578b\u662f\u4e00\u79cd\u65e0\u76d1\u7763\u7684\u673a\u5668\u5b66\u4e60\u6280\u672f\uff0c\u7528\u4e8e\u4ece\u5927\u91cf\u6587\u6863\u4e2d\u53d1\u73b0\u6f5c\u5728\u7684\u4e3b\u9898\u3002\u5e38\u89c1\u7684\u4e3b\u9898\u6a21\u578b\u6709LDA\uff08Latent Dirichlet Allocation\uff09\u7b49\u3002<\/p>\n<\/p>\n
    4.1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h2>\n<\/p>\n
    from sklearn.decomposition import LatentDirichletAllocation<\/p>\n
    from sklearn.feature_extraction.text import CountVectorizer<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4.2\u3001\u51c6\u5907\u6587\u672c\u6570\u636e<\/h2>\n<\/p>\n
    documents = [<\/p>\n
        "Python is a high-level programming language.",<\/p>\n
        "Machine learning and data science are applications of Python.",<\/p>\n
        "Python is popular for web development.",<\/p>\n
        "Data analysis and machine learning are key applications of Python."<\/p>\n
    ]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4.3\u3001\u8f6c\u6362\u6587\u672c\u6570\u636e<\/h2>\n<\/p>\n
    vectorizer = CountVectorizer()<\/p>\n
    data_vectorized = vectorizer.fit_transform(documents)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4.4\u3001\u8bad\u7ec3LDA\u6a21\u578b<\/h2>\n<\/p>\n
    lda_model = LatentDirichletAllocation(n_components=2, random_state=42)<\/p>\n
    lda_model.fit(data_vectorized)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4.5\u3001\u663e\u793a\u4e3b\u9898\u5173\u952e\u8bcd<\/h2>\n<\/p>\n
    def print_top_words(model, feature_names, n_top_words):<\/p>\n
        for topic_idx, topic in enumerate(model.components_):<\/p>\n
            print("Topic #%d:" % topic_idx)<\/p>\n
            print(" ".join([feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]]))<\/p>\n
        print()<\/p>\n
    tf_feature_names = vectorizer.get_feature_names_out()<\/p>\n
    print_top_words(lda_model, tf_feature_names, 5)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u4f7f\u7528\u673a\u5668\u5b66\u4e60<\/strong><\/h2>\n
    \u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u53ef\u4ee5\u901a\u8fc7\u8bad\u7ec3\u6a21\u578b\u6765\u81ea\u52a8\u62bd\u53d6\u6587\u672c\u4e2d\u7684\u5173\u952e\u5b57\u3002\u5e38\u89c1\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u5305\u62ec\u76d1\u7763\u5b66\u4e60\u548c\u65e0\u76d1\u7763\u5b66\u4e60\u7b49\u3002<\/p>\n<\/p>\n
    5.1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h2>\n<\/p>\n
    from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
    from sklearn.cluster import KMeans<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    5.2\u3001\u51c6\u5907\u6587\u672c\u6570\u636e<\/h2>\n<\/p>\n
    documents = [<\/p>\n
        "Python is a high-level programming language.",<\/p>\n
        "Machine learning and data science are applications of Python.",<\/p>\n
        "Python is popular for web development.",<\/p>\n
        "Data analysis and machine learning are key applications of Python."<\/p>\n
    ]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    5.3\u3001\u8f6c\u6362\u6587\u672c\u6570\u636e<\/h2>\n<\/p>\n
    vectorizer = TfidfVectorizer()<\/p>\n
    X = vectorizer.fit_transform(documents)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    5.4\u3001\u8bad\u7ec3KMeans\u6a21\u578b<\/h2>\n<\/p>\n
    kmeans = KMeans(n_clusters=2, random_state=42)<\/p>\n
    kmeans.fit(X)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    5.5\u3001\u663e\u793a\u805a\u7c7b\u7ed3\u679c<\/h2>\n<\/p>\n
    order_centroids = kmeans.cluster_centers_.argsort()[:, ::-1]<\/p>\n
    terms = vectorizer.get_feature_names_out()<\/p>\n
    for i in range(2):<\/p>\n
        print("Cluster %d:" % i),<\/p>\n
        for ind in order_centroids[i, :5]:<\/p>\n
            print(' %s' % terms[ind])<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u62bd\u53d6\u6587\u672c\u4e2d\u7684\u5173\u952e\u5b57\u3002\u4e0d\u540c\u7684\u65b9\u6cd5\u6709\u5404\u81ea\u7684\u4f18\u7f3a\u70b9\uff0c\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u6570\u636e\u7279\u70b9\u3002\u65e0\u8bba\u662f\u4f7f\u7528TF-IDF\u7b97\u6cd5\u3001TextRank\u7b97\u6cd5\u3001\u5206\u8bcd\u5de5\u5177\u3001\u4e3b\u9898\u6a21\u578b\u8fd8\u662f\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\uff0c\u90fd\u9700\u8981\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u8fdb\u884c\u9002\u5f53\u7684\u8c03\u6574\u548c\u4f18\u5316\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684Python\u5e93\u6765\u8fdb\u884c\u5173\u952e\u5b57\u62bd\u53d6\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u6709\u591a\u79cd\u5e93\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u5173\u952e\u5b57\u62bd\u53d6\uff0c\u5982NLTK\u3001spaCy\u548cGensim\u7b49\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u4e3b\u8981\u53d6\u51b3\u4e8e\u4f60\u7684\u9700\u6c42\u3002\u4f8b\u5982\uff0cNLTK\u9002\u5408\u57fa\u7840\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\uff0cspaCy\u5219\u66f4\u9002\u5408\u5904\u7406\u5927\u578b\u6587\u672c\u6570\u636e\u5e76\u5177\u6709\u66f4\u9ad8\u7684\u6027\u80fd\uff0c\u800cGensim\u5728\u4e3b\u9898\u5efa\u6a21\u548c\u8bcd\u5411\u91cf\u65b9\u9762\u8868\u73b0\u7a81\u51fa\u3002\u4e86\u89e3\u5404\u4e2a\u5e93\u7684\u7279\u6027\u548c\u5e94\u7528\u573a\u666f\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4f60\u505a\u51fa\u66f4\u660e\u667a\u7684\u9009\u62e9\u3002<\/p>\n
    \u5173\u952e\u5b57\u62bd\u53d6\u7684\u5e38\u7528\u7b97\u6cd5\u6709\u54ea\u4e9b\uff1f<\/strong>
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