{"id":1071263,"date":"2025-01-08T11:05:44","date_gmt":"2025-01-08T03:05:44","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1071263.html"},"modified":"2025-01-08T11:05:46","modified_gmt":"2025-01-08T03:05:46","slug":"python%e5%a6%82%e4%bd%95%e6%8f%90%e5%8f%96%e8%af%84%e8%ae%ba%e4%b8%ad%e5%85%b3%e9%94%ae%e8%af%8d-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1071263.html","title":{"rendered":"python\u5982\u4f55\u63d0\u53d6\u8bc4\u8bba\u4e2d\u5173\u952e\u8bcd"},"content":{"rendered":"

\"python\u5982\u4f55\u63d0\u53d6\u8bc4\u8bba\u4e2d\u5173\u952e\u8bcd\"<\/p>\n

\u5728Python\u4e2d\u63d0\u53d6\u8bc4\u8bba\u4e2d\u7684\u5173\u952e\u8bcd\uff0c\u53ef\u4ee5\u4f7f\u7528\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u6280\u672f\uff0c\u5982TF-IDF\u3001RAKE\u3001TextRank\u7b49\u65b9\u6cd5\u3002<\/strong>\u5176\u4e2d\uff0cTF-IDF\uff08\u8bcd\u9891-\u9006\u6587\u6863\u9891\u7387\uff09\u662f\u4e00\u79cd\u7b80\u5355\u4e14\u6709\u6548\u7684\u5173\u952e\u8bcd\u63d0\u53d6\u65b9\u6cd5\uff0c\u5b83\u901a\u8fc7\u8ba1\u7b97\u8bcd\u9891\u548c\u9006\u6587\u6863\u9891\u7387\u6765\u8861\u91cf\u5355\u8bcd\u7684\u91cd\u8981\u6027\u3002RAKE\uff08Rapid Automatic Keyword Extraction\uff09\u662f\u4e00\u79cd\u57fa\u4e8e\u8bcd\u6c47\u5171\u73b0\u7684\u5173\u952e\u8bcd\u63d0\u53d6\u65b9\u6cd5\u3002TextRank\u662f\u4e00\u79cd\u57fa\u4e8e\u56fe\u7684\u6392\u5e8f\u7b97\u6cd5\u3002<\/p>\n<\/p>\n

\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bba\u5982\u4f55\u4f7f\u7528Python\u63d0\u53d6\u8bc4\u8bba\u4e2d\u7684\u5173\u952e\u8bcd\uff0c\u7279\u522b\u662fTF-IDF\u65b9\u6cd5\uff0cRAKE\u65b9\u6cd5\u548cTextRank\u65b9\u6cd5\u7684\u5b9e\u73b0\u3002<\/p>\n<\/p>\n

\u4e00\u3001TF-IDF\u65b9\u6cd5<\/h3>\n<\/p>\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\u4e00\u4e2a\u6587\u6863\u4e2d\u7684\u91cd\u8981\u6027\u3002\u5b83\u7684\u57fa\u672c\u601d\u60f3\u662f\uff0c\u5982\u679c\u4e00\u4e2a\u8bcd\u5728\u4e00\u4e2a\u6587\u6863\u4e2d\u51fa\u73b0\u7684\u9891\u7387\u9ad8\uff0c\u4f46\u662f\u5728\u5176\u4ed6\u6587\u6863\u4e2d\u51fa\u73b0\u7684\u9891\u7387\u4f4e\uff0c\u90a3\u4e48\u8fd9\u4e2a\u8bcd\u5728\u8fd9\u4e2a\u6587\u6863\u4e2d\u5c31\u5177\u6709\u5f88\u9ad8\u7684\u91cd\u8981\u6027\u3002<\/p>\n<\/p>\n

1\u3001\u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n

\u8981\u4f7f\u7528TF-IDF\u65b9\u6cd5\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5scikit-learn<\/code>\u5e93\uff0c\u8fd9\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u673a\u5668\u5b66\u4e60<\/a>\u5e93\u3002<\/p>\n<\/p>\n

pip install scikit-learn<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165\u76f8\u5173\u5e93\u5e76\u52a0\u8f7d\u6570\u636e<\/h4>\n<\/p>\n
from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
\u793a\u4f8b\u8bc4\u8bba\u6570\u636e<\/strong><\/h2>\n
comments = [<\/p>\n
    "\u8fd9\u4e2a\u4ea7\u54c1\u975e\u5e38\u597d\uff0c\u6211\u5f88\u559c\u6b22",<\/p>\n
    "\u8d28\u91cf\u4e0d\u9519\uff0c\u4ef7\u683c\u4e5f\u5408\u7406",<\/p>\n
    "\u4e0d\u6ee1\u610f\uff0c\u670d\u52a1\u6001\u5ea6\u5dee\uff0c\u8d28\u91cf\u4e00\u822c",<\/p>\n
    "\u6027\u4ef7\u6bd4\u9ad8\uff0c\u503c\u5f97\u8d2d\u4e70",<\/p>\n
    "\u975e\u5e38\u4e0d\u6ee1\u610f\uff0c\u9000\u8d27\u4e86"<\/p>\n
]<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8ba1\u7b97TF-IDF\u503c<\/h4>\n<\/p>\n
# \u521b\u5efaTfidfVectorizer\u5bf9\u8c61<\/p>\n
vectorizer = TfidfVectorizer()<\/p>\n
\u8ba1\u7b97TF-IDF\u503c<\/strong><\/h2>\n
tfidf_matrix = vectorizer.fit_transform(comments)<\/p>\n
\u83b7\u53d6\u8bcd\u6c47\u8868<\/strong><\/h2>\n
feature_names = vectorizer.get_feature_names_out()<\/p>\n
\u6253\u5370\u6bcf\u4e2a\u8bc4\u8bba\u7684TF-IDF\u503c<\/strong><\/h2>\n
for i, comment in enumerate(comments):<\/p>\n
    print(f"Comment {i+1}:")<\/p>\n
    tfidf_scores = zip(feature_names, tfidf_matrix[i].toarray()[0])<\/p>\n
    sorted_tfidf_scores = sorted(tfidf_scores, key=lambda x: x[1], reverse=True)<\/p>\n
    for word, score in sorted_tfidf_scores:<\/p>\n
        if score > 0:<\/p>\n
            print(f"    {word}: {score:.4f}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6bcf\u4e2a\u8bc4\u8bba\u4e2d\u7684\u5173\u952e\u8bcd\u53ca\u5176TF-IDF\u503c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001RAKE\u65b9\u6cd5<\/h3>\n<\/p>\n
RAKE\uff08Rapid Automatic Keyword Extraction\uff09\u662f\u4e00\u79cd\u7b80\u5355\u4e14\u9ad8\u6548\u7684\u5173\u952e\u8bcd\u63d0\u53d6\u7b97\u6cd5\u3002\u5b83\u901a\u8fc7\u5206\u6790\u8bcd\u6c47\u7684\u5171\u73b0\u5173\u7cfb\u6765\u63d0\u53d6\u5173\u952e\u8bcd\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u8981\u4f7f\u7528RAKE\u65b9\u6cd5\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5rake-nltk<\/code>\u5e93\u3002<\/p>\n<\/p>\n
pip install rake-nltk<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165\u76f8\u5173\u5e93\u5e76\u52a0\u8f7d\u6570\u636e<\/h4>\n<\/p>\n
from rake_nltk import Rake<\/p>\n
\u793a\u4f8b\u8bc4\u8bba\u6570\u636e<\/strong><\/h2>\n
comments = [<\/p>\n
    "\u8fd9\u4e2a\u4ea7\u54c1\u975e\u5e38\u597d\uff0c\u6211\u5f88\u559c\u6b22",<\/p>\n
    "\u8d28\u91cf\u4e0d\u9519\uff0c\u4ef7\u683c\u4e5f\u5408\u7406",<\/p>\n
    "\u4e0d\u6ee1\u610f\uff0c\u670d\u52a1\u6001\u5ea6\u5dee\uff0c\u8d28\u91cf\u4e00\u822c",<\/p>\n
    "\u6027\u4ef7\u6bd4\u9ad8\uff0c\u503c\u5f97\u8d2d\u4e70",<\/p>\n
    "\u975e\u5e38\u4e0d\u6ee1\u610f\uff0c\u9000\u8d27\u4e86"<\/p>\n
]<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u63d0\u53d6\u5173\u952e\u8bcd<\/h4>\n<\/p>\n
# \u521b\u5efaRake\u5bf9\u8c61<\/p>\n
rake = Rake()<\/p>\n
\u63d0\u53d6\u6bcf\u4e2a\u8bc4\u8bba\u7684\u5173\u952e\u8bcd<\/strong><\/h2>\n
for i, comment in enumerate(comments):<\/p>\n
    rake.extract_keywords_from_text(comment)<\/p>\n
    keywords = rake.get_ranked_phrases()<\/p>\n
    print(f"Comment {i+1} Keywords: {keywords}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6bcf\u4e2a\u8bc4\u8bba\u4e2d\u7684\u5173\u952e\u8bcd\u3002<\/p>\n<\/p>\n
\u4e09\u3001TextRank\u65b9\u6cd5<\/h3>\n<\/p>\n
TextRank\u662f\u4e00\u79cd\u57fa\u4e8e\u56fe\u7684\u6392\u5e8f\u7b97\u6cd5\uff0c\u7c7b\u4f3c\u4e8ePageRank\u7b97\u6cd5\u3002\u5b83\u901a\u8fc7\u6784\u5efa\u8bcd\u6c47\u56fe\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u8282\u70b9\u7684PageRank\u503c\u6765\u63d0\u53d6\u5173\u952e\u8bcd\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u8981\u4f7f\u7528TextRank\u65b9\u6cd5\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5gensim<\/code>\u5e93\u3002<\/p>\n<\/p>\n
pip install gensim<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165\u76f8\u5173\u5e93\u5e76\u52a0\u8f7d\u6570\u636e<\/h4>\n<\/p>\n
from gensim.summarization import keywords<\/p>\n
\u793a\u4f8b\u8bc4\u8bba\u6570\u636e<\/strong><\/h2>\n
comments = [<\/p>\n
    "\u8fd9\u4e2a\u4ea7\u54c1\u975e\u5e38\u597d\uff0c\u6211\u5f88\u559c\u6b22",<\/p>\n
    "\u8d28\u91cf\u4e0d\u9519\uff0c\u4ef7\u683c\u4e5f\u5408\u7406",<\/p>\n
    "\u4e0d\u6ee1\u610f\uff0c\u670d\u52a1\u6001\u5ea6\u5dee\uff0c\u8d28\u91cf\u4e00\u822c",<\/p>\n
    "\u6027\u4ef7\u6bd4\u9ad8\uff0c\u503c\u5f97\u8d2d\u4e70",<\/p>\n
    "\u975e\u5e38\u4e0d\u6ee1\u610f\uff0c\u9000\u8d27\u4e86"<\/p>\n
]<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u63d0\u53d6\u5173\u952e\u8bcd<\/h4>\n<\/p>\n
# \u63d0\u53d6\u6bcf\u4e2a\u8bc4\u8bba\u7684\u5173\u952e\u8bcd<\/p>\n
for i, comment in enumerate(comments):<\/p>\n
    key_words = keywords(comment, words=5, split=True, lemmatize=True)<\/p>\n
    print(f"Comment {i+1} Keywords: {key_words}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6bcf\u4e2a\u8bc4\u8bba\u4e2d\u7684\u5173\u952e\u8bcd\u3002<\/p>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8ba8\u8bba\u4e86\u4e09\u79cd\u5e38\u7528\u7684\u5173\u952e\u8bcd\u63d0\u53d6\u65b9\u6cd5\uff1aTF-IDF\u3001RAKE\u548cTextRank\u3002TF-IDF\u901a\u8fc7\u8ba1\u7b97\u8bcd\u9891\u548c\u9006\u6587\u6863\u9891\u7387\u6765\u8861\u91cf\u5355\u8bcd\u7684\u91cd\u8981\u6027<\/strong>\uff0cRAKE\u901a\u8fc7\u5206\u6790\u8bcd\u6c47\u7684\u5171\u73b0\u5173\u7cfb\u6765\u63d0\u53d6\u5173\u952e\u8bcd<\/strong>\uff0cTextRank\u901a\u8fc7\u6784\u5efa\u8bcd\u6c47\u56fe\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u8282\u70b9\u7684PageRank\u503c\u6765\u63d0\u53d6\u5173\u952e\u8bcd<\/strong>\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u70b9\u548c\u9002\u7528\u573a\u666f\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53ef\u4ee5\u66f4\u597d\u5730\u63d0\u53d6\u8bc4\u8bba\u4e2d\u7684\u5173\u952e\u8bcd\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u793a\u4f8b\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5728Python\u4e2d\u5b9e\u73b0\u8fd9\u4e9b\u5173\u952e\u8bcd\u63d0\u53d6\u65b9\u6cd5\uff0c\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u8bc4\u8bba\u5185\u5bb9\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff01<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u63d0\u53d6\u8bc4\u8bba\u4e2d\u7684\u5173\u952e\u8bcd\uff1f<\/strong>
\u4f7f\u7528Python\u63d0\u53d6\u8bc4\u8bba\u4e2d\u7684\u5173\u952e\u8bcd\u901a\u5e38\u53ef\u4ee5\u901a\u8fc7\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u5e93\u6765\u5b9e\u73b0\u3002\u5e38\u89c1\u7684\u5e93\u5305\u62ecNLTK\u3001spaCy\u548cGensim\u3002\u60a8\u53ef\u4ee5\u5148\u5bf9\u8bc4\u8bba\u8fdb\u884c\u9884\u5904\u7406\uff0c\u5982\u53bb\u9664\u505c\u7528\u8bcd\u548c\u6807\u70b9\u7b26\u53f7\uff0c\u7136\u540e\u4f7f\u7528TF-IDF\u6216\u8bcd\u9891\u5206\u6790\u7b49\u65b9\u6cd5\u63d0\u53d6\u5173\u952e\u8bcd\u3002\u6b64\u5916\uff0c\u4f7f\u7528TextRank\u7b97\u6cd5\u4e5f\u662f\u4e00\u79cd\u6709\u6548\u7684\u63d0\u53d6\u5173\u952e\u8bcd\u7684\u65b9\u5f0f\u3002<\/p>\n
\u63d0\u53d6\u5173\u952e\u8bcd\u65f6\u9700\u8981\u8003\u8651\u54ea\u4e9b\u9884\u5904\u7406\u6b65\u9aa4\uff1f<\/strong>
\u5728\u63d0\u53d6\u5173\u952e\u8bcd\u4e4b\u524d\uff0c\u8fdb\u884c\u9002\u5f53\u7684\u6587\u672c\u9884\u5904\u7406\u975e\u5e38\u91cd\u8981\u3002\u5e38\u89c1\u7684\u6b65\u9aa4\u5305\u62ec\uff1a\u5c0f\u5199\u5316\u6587\u672c\u3001\u53bb\u9664\u505c\u7528\u8bcd\uff08\u5982\u201c\u7684\u201d\u3001\u201c\u4e86\u201d\u7b49\u65e0\u610f\u4e49\u8bcd\u6c47\uff09\u3001\u53bb\u9664\u6807\u70b9\u7b26\u53f7\u3001\u8bcd\u5e72\u63d0\u53d6\uff08\u5c06\u8bcd\u6c47\u8fd8\u539f\u4e3a\u57fa\u672c\u5f62\u5f0f\uff09\u4ee5\u53ca\u5206\u8bcd\u5904\u7406\u3002\u8fd9\u4e9b\u6b65\u9aa4\u53ef\u4ee5\u63d0\u9ad8\u5173\u952e\u8bcd\u63d0\u53d6\u7684\u51c6\u786e\u6027\u548c\u6709\u6548\u6027\u3002<\/p>\n
\u6709\u54ea\u4e9b\u5e38\u7528\u7684Python\u5e93\u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u5173\u952e\u8bcd\u63d0\u53d6\uff1f<\/strong>
\u6709\u591a\u4e2aPython\u5e93\u53ef\u4ee5\u5e2e\u52a9\u60a8\u63d0\u53d6\u5173\u952e\u8bcd\u3002NLTK\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6587\u672c\u5904\u7406\u529f\u80fd\u3002spaCy\u5219\u4ee5\u5176\u9ad8\u6548\u548c\u51c6\u786e\u6027\u8457\u79f0\uff0c\u9002\u5408\u5927\u89c4\u6a21\u6587\u672c\u5904\u7406\u3002Gensim\u4e13\u6ce8\u4e8e\u4e3b\u9898\u5efa\u6a21\u548c\u5411\u91cf\u5316\u8868\u793a\uff0c\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5173\u952e\u8bcd\u63d0\u53d6\u3002\u6b64\u5916\uff0cjieba\u662f\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u4e2d\u6587\u5206\u8bcd\u5e93\uff0c\u9002\u5408\u5904\u7406\u4e2d\u6587\u8bc4\u8bba\u3002<\/p>\n
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