{"id":549160,"date":"2025-02-26T03:00:00","date_gmt":"2025-02-26T02:00:00","guid":{"rendered":"https:\/\/blog.jetbrains.com\/?post_type=pycharm&#038;p=549160"},"modified":"2025-02-26T09:29:08","modified_gmt":"2025-02-26T08:29:08","slug":"introduction-to-sentiment-analysis-in-python","status":"publish","type":"pycharm","link":"https:\/\/blog.jetbrains.com\/zh-hans\/pycharm\/2025\/02\/introduction-to-sentiment-analysis-in-python\/","title":{"rendered":"Python \u60c5\u611f\u5206\u6790\u7b80\u4ecb"},"content":{"rendered":"<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-549212 size-full\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2025\/02\/pc-featured_blog_1280x720_en-3.png\" alt=\"\" width=\"2560\" height=\"1440\" \/><\/figure>\n<p>\u60c5\u611f\u5206\u6790\u662f\u6700\u6d41\u884c\u7684\u6587\u672c\u5206\u6790\u65b9\u5f0f\u4e4b\u4e00\u3002 \u5b83\u80fd\u8ba9\u6211\u4eec\u76f4\u89c2\u5730\u4e86\u89e3\u4eba\u4eec\u5728\u591a\u79cd\u9886\u57df\u7684\u611f\u53d7\uff0c\u5e76\u5728\u5ba2\u6237\u670d\u52a1\u3001\u5e02\u573a\u4e0e\u4ea7\u54c1\u7814\u7a76\u4ee5\u53ca\u7ade\u4e89\u5206\u6790\u7b49\u65b9\u9762\u5177\u6709\u5b9e\u9645\u5e94\u7528\u3002<\/p>\n<p>\u50cf\u5176\u4ed6\u81ea\u7136\u8bed\u8a00\u5904\u7406 (NLP) \u9886\u57df\u4e00\u6837\uff0c\u60c5\u611f\u5206\u6790\u4e5f\u53ef\u80fd\u4f1a\u6709\u4e9b\u590d\u6742\u3002 \u597d\u5728 <a href=\"https:\/\/www.jetbrains.com.cn\/en-us\/guide\/python\/\" target=\"_blank\" rel=\"noopener\">Python<\/a> \u62e5\u6709\u51fa\u8272\u7684\u8f6f\u4ef6\u5305\u548c\u5de5\u5177\uff0c\u4f7f NLP \u7684\u8fd9\u4e00\u5206\u652f\u66f4\u6613\u7406\u89e3\u3002<\/p>\n<p>\u5728\u8fd9\u7bc7\u535a\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u63a2\u8ba8\u4e00\u4e9b\u6700\u6d41\u884c\u7684 Python \u60c5\u611f\u5206\u6790\u8f6f\u4ef6\u5305\u3001\u5b83\u4eec\u7684\u8fd0\u4f5c\u65b9\u5f0f\uff0c\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528\u524d\u6cbf\u6280\u672f\u8bad\u7ec3\u60a8\u81ea\u5df1\u7684\u60c5\u611f\u5206\u6790\u6a21\u578b\u3002 \u6211\u4eec\u8fd8\u5c06\u5206\u6790\u4e00\u4e9b\u4f7f\u8fd9\u4e9b\u8f6f\u4ef6\u5305\u66f4\u6613\u7528\u3001\u66f4\u5feb\u901f\u7684 <a href=\"https:\/\/www.jetbrains.com.cn\/pycharm\/data-science\/\" target=\"_blank\" rel=\"noopener\" data-type=\"link\" data-id=\"https:\/\/www.jetbrains.com\/pycharm\/data-science\/\">PyCharm<\/a> \u529f\u80fd\u3002<\/p>\n<h2 class=\"wp-block-heading\">\u4ec0\u4e48\u662f\u60c5\u611f\u5206\u6790\uff1f<\/h2>\n<p>\u60c5\u611f\u5206\u6790\u662f\u5206\u6790\u4e00\u6bb5\u6587\u672c\u4ee5\u786e\u5b9a\u5176\u60c5\u7eea\u57fa\u8c03\u7684\u8fc7\u7a0b\u3002 \u4ece\u8fd9\u4e2a\u5b9a\u4e49\u4e2d\u60a8\u53ef\u80fd\u53ef\u4ee5\u770b\u51fa\uff0c\u60c5\u611f\u5206\u6790\u9886\u57df\u975e\u5e38\u5e7f\u6cdb\uff0c\u878d\u5408\u4e86\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u5185\u7684\u591a\u79cd\u65b9\u6cd5\u3002<\/p>\n<p>\u60a8\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9a\u4e49\u201c\u60c5\u7eea\u57fa\u8c03\u201d\u3002 \u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u786e\u5b9a\u4e00\u6bb5\u6587\u672c\u7684<em>\u6548\u4ef7\uff08valence\uff09<\/em>\u6216<em>\u6781\u6027\uff08polarity\uff09<\/em>\uff0c\u5373\u6587\u672c\u4e2d\u8868\u8fbe\u7684\u60c5\u611f\u662f\u6b63\u9762\u8fd8\u662f\u8d1f\u9762\u3002 \u60c5\u7eea\u57fa\u8c03\u901a\u5e38\u4e5f\u88ab\u89c6\u4e3a\u6587\u672c\u5206\u7c7b\u95ee\u9898\uff0c\u5176\u4e2d\u6587\u672c\u88ab\u5f52\u7c7b\u4e3a\u6b63\u9762\u6216\u8d1f\u9762\u3002<\/p>\n<p>\u4ee5\u5982\u4e0b <a href=\"https:\/\/www.amazon.com\/AmazonBasics-12-Cup-Coffee-Reusable-Stainless\/dp\/B084ZH769P\/ref=sr_1_1_ffob_sspa?th=1\" target=\"_blank\" rel=\"noopener\">Amazon \u4ea7\u54c1\u8bc4\u4ef7<\/a>\u4e3a\u4f8b\uff1a<\/p>\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532284\" style=\"aspect-ratio: 3.373529411764706; width: 840px; height: auto;\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/11\/Screenshot-2024-07-24-at-15.14.00.png\" alt=\"\" width=\"2294\" height=\"680\" \/><\/figure>\n<p>\u8fd9\u663e\u7136\u4e0d\u662f\u4e00\u4e2a\u6ee1\u610f\u7684\u5ba2\u6237\uff0c\u60c5\u611f\u5206\u6790\u6280\u672f\u4f1a\u5c06\u8fd9\u6761\u8bc4\u4ef7\u5f52\u7c7b\u4e3a\u8d1f\u9762\u3002<\/p>\n<p>\u4e0e\u4e4b\u76f8\u5bf9\uff0c\u6765\u770b\u4e00\u4f4d\u66f4\u6ee1\u610f\u7684\u4e70\u5bb6\uff1a<\/p>\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532295\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/11\/Screenshot-2024-07-24-at-15.13.21.png\" alt=\"\" width=\"1566\" height=\"408\" \/><\/figure>\n<p>\u8fd9\u4e00\u6b21\uff0c\u60c5\u611f\u5206\u6790\u6280\u672f\u4f1a\u5c06\u5176\u5f52\u7c7b\u4e3a\u6b63\u9762\u3002<\/p>\n<h3 class=\"wp-block-heading\">\u4e0d\u540c\u7c7b\u578b\u7684\u60c5\u611f\u5206\u6790<\/h3>\n<p>\u60a8\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u4ece\u6587\u672c\u4e2d\u63d0\u53d6\u60c5\u7eea\u4fe1\u606f\u3002 \u6211\u4eec\u6765\u770b\u5176\u4e2d\u6700\u91cd\u8981\u7684\u51e0\u79cd\u3002<\/p>\n<h4 class=\"wp-block-heading\">\u5b9a\u4e49\u60c5\u611f\u7684\u65b9\u5f0f<\/h4>\n<p>\u9996\u5148\uff0c\u60c5\u611f\u5206\u6790\u65b9\u5f0f\u6709\u591a\u79cd\u5b9a\u4e49\u60c5\u611f\u6216\u60c5\u7eea\u7684\u65b9\u5f0f\u3002<\/p>\n<p><strong>\u4e8c\u5206\u7c7b<\/strong>\uff1a\u5c06\u6587\u6863\u7684\u6548\u4ef7\u5206\u4e3a\u4e24\u7c7b\uff0c\u5373<em>\u6b63\u9762<\/em>\u6216<em>\u8d1f\u9762<\/em>\uff0c\u5982 <a href=\"https:\/\/huggingface.co\/datasets\/stanfordnlp\/sst2\" target=\"_blank\" rel=\"noopener\">SST-2 \u6570\u636e\u96c6<\/a>\u3002 \u4e0e\u6b64\u76f8\u5173\u7684\u662f\u6548\u4ef7\u7684\u5206\u7c7b\uff0c\u6dfb\u52a0<em>\u4e2d\u7acb<\/em>\u7c7b\uff08\u6587\u672c\u4e0d\u8868\u8fbe\u6709\u5173\u67d0\u4e2a\u4e3b\u9898\u7684\u60c5\u611f\uff09\u751a\u81f3<em>\u51b2\u7a81<\/em>\u7c7b\uff08\u6587\u672c\u540c\u65f6\u8868\u8fbe\u6709\u5173\u67d0\u4e2a\u4e3b\u9898\u7684\u6b63\u9762\u548c\u8d1f\u9762\u60c5\u611f\uff09\u3002<\/p>\n<p>\u4e00\u4e9b\u60c5\u611f\u5206\u6790\u5668\u4f7f\u7528\u76f8\u5173\u8861\u91cf\u65b9\u6cd5\u5c06\u6587\u672c\u5206\u4e3a<em>\u4e3b\u89c2<\/em>\u6216<em>\u5ba2\u89c2<\/em>\u3002<\/p>\n<p><strong>\u7ec6\u7c92\u5ea6<\/strong>\uff1a\u8fd9\u4e2a\u672f\u8bed\u63cf\u8ff0\u4e86\u51e0\u79cd\u4e0d\u540c\u7684\u60c5\u611f\u5206\u6790\u65b9\u5f0f\uff0c\u4f46\u5728\u8fd9\u91cc\u6307\u7684\u662f\u5c06\u6b63\u4ef7\u548c\u8d1f\u4ef7\u5206\u89e3\u4e3a\u674e\u514b\u7279\u91cf\u8868\u3002 <a href=\"https:\/\/huggingface.co\/datasets\/SetFit\/sst5\" target=\"_blank\" rel=\"noopener\">SST-5 \u6570\u636e\u96c6<\/a>\u662f\u4e00\u4e2a\u8457\u540d\u7684\u4f8b\u5b50\uff0c\u5b83\u4f7f\u7528\u4e94\u70b9\u674e\u514b\u7279\u91cf\u8868\uff0c\u7c7b\u522b\u5305\u62ec<em>\u975e\u5e38\u6b63\u9762<\/em>\u3001<em>\u6b63\u9762<\/em>\u3001<em>\u4e2d\u7acb<\/em>\u3001<em>\u8d1f\u9762<\/em>\u548c<em>\u975e\u5e38\u8d1f\u9762<\/em>\u3002<\/p>\n<p><strong>\u8fde\u7eed<\/strong>\uff1a\u4e00\u6bb5\u6587\u672c\u7684\u6548\u4ef7\u4e5f\u53ef\u4ee5\u88ab\u8fde\u7eed\u8861\u91cf\uff0c\u5206\u6570\u8868\u660e\u4f5c\u8005\u7684\u60c5\u611f\u662f\u6b63\u9762\u8fd8\u662f\u8d1f\u9762\u3002 \u4f8b\u5982\uff0c<a href=\"https:\/\/github.com\/cjhutto\/vaderSentiment\" target=\"_blank\" rel=\"noopener\">VADER \u60c5\u611f\u5206\u6790\u5668<\/a>\u4f1a\u7ed9\u4e00\u6bb5\u6587\u672c\u4e00\u4e2a\u4ecb\u4e8e \u20131\uff08<em>\u5f3a\u70c8\u8d1f\u9762<\/em>\uff09\u548c 1\uff08<em>\u5f3a\u70c8\u6b63\u9762<\/em>\uff09\u4e4b\u95f4\u7684\u5206\u6570\uff0c\u63a5\u8fd1 0 \u7684\u5206\u6570\u8868\u793a\u4e2d\u7acb\u60c5\u611f\u3002<\/p>\n<p><strong>\u57fa\u4e8e\u60c5\u7eea<\/strong>\uff1a\u4e5f\u79f0\u4e3a\u60c5\u7eea\u68c0\u6d4b\u6216\u60c5\u7eea\u8bc6\u522b\uff0c\u8fd9\u79cd\u65b9\u5f0f\u5c1d\u8bd5\u68c0\u6d4b\u4e00\u6bb5\u6587\u672c\u4e2d\u8868\u8fbe\u7684\u7279\u5b9a\u60c5\u7eea\u3002 \u60a8\u53ef\u4ee5\u901a\u8fc7\u4e24\u79cd\u65b9\u5f0f\u6765\u5b9e\u73b0\u3002 \u5206\u7c7b\u60c5\u7eea\u68c0\u6d4b\u5c1d\u8bd5\u5c06\u6587\u672c\u8868\u8fbe\u7684\u60c5\u611f\u5206\u4e3a\u51e0\u79cd\u79bb\u6563\u60c5\u7eea\u4e4b\u4e00\uff0c\u901a\u5e38\u57fa\u4e8e<a href=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/02699939208411068\" target=\"_blank\" rel=\"noopener\">\u57c3\u514b\u66fc<\/a>\u6a21\u578b\uff0c\u5305\u62ec<em>\u6124\u6012<\/em>\u3001<em>\u538c\u6076<\/em>\u3001<em>\u6050\u60e7<\/em>\u3001<em>\u559c\u60a6<\/em>\u3001<em>\u60b2\u4f24<\/em>\u548c<em>\u60ca\u8bb6<\/em>\u3002 \u7528\u4e8e\u6b64\u7c7b\u60c5\u7eea\u68c0\u6d4b\u7684<a href=\"https:\/\/huggingface.co\/j-hartmann\/emotion-english-distilroberta-base#appendix-%F0%9F%93%9A\" target=\"_blank\" rel=\"noopener\">\u6570\u636e\u96c6\u6709\u5f88\u591a<\/a>\u3002 \u7ef4\u5ea6\u60c5\u7eea\u68c0\u6d4b\u5728\u60c5\u611f\u5206\u6790\u4e2d\u4e0d\u592a\u5e38\u7528\uff0c\u8861\u91cf\u7684\u662f\u4e00\u6bb5\u6587\u672c\u7684<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s12144-014-9219-4\" target=\"_blank\" rel=\"noopener\">\u4e09\u4e2a\u60c5\u7eea\u5207\u9762<\/a>\uff1a<em>\u6781\u6027<\/em>\u3001<em>\u5524\u9192<\/em>\uff08\u611f\u89c9\u6709\u591a\u523a\u6fc0\uff09\u548c<em>\u652f\u914d<\/em>\uff08\u60c5\u7eea\u8868\u8fbe\u6709\u591a\u53d7\u9650\uff09\u3002<\/p>\n<h4 class=\"wp-block-heading\">\u5206\u6790\u7ea7\u522b<\/h4>\n<p>\u6211\u4eec\u8fd8\u53ef\u4ee5\u8003\u8651\u5206\u6790\u4e00\u6bb5\u6587\u672c\u7684\u4e0d\u540c\u7ea7\u522b\u3002 \u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\uff0c\u6211\u4eec\u6765\u770b\u53e6\u4e00\u6761\u5bf9\u5496\u5561\u673a\u7684\u8bc4\u4ef7\uff1a<\/p>\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532307\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/11\/Screenshot-2024-07-24-at-16.43.21.png\" alt=\"\" width=\"1438\" height=\"374\" \/><\/figure>\n<p><strong>\u6587\u6863\u7ea7\u522b<\/strong>\uff1a\u8fd9\u662f\u6700\u57fa\u672c\u7684\u5206\u6790\u7ea7\u522b\uff0c\u5c06\u8fd4\u56de\u6574\u6bb5\u6587\u672c\u7684\u4e00\u79cd\u60c5\u611f\u3002 \u6587\u6863\u7ea7\u5206\u6790\u53ef\u80fd\u9002\u7528\u4e8e\u975e\u5e38\u77ed\u7684\u6587\u672c\uff0c\u4f8b\u5982\u63a8\u6587\uff0c\u4f46\u5982\u679c\u5b58\u5728\u6df7\u5408\u60c5\u611f\uff0c\u5219\u53ef\u80fd\u7ed9\u51fa\u8bef\u5bfc\u6027\u7b54\u6848\u3002 \u4f8b\u5982\uff0c\u5982\u679c\u6211\u4eec\u6839\u636e\u6574\u4e2a\u6587\u6863\u5bf9\u6b64\u8bc4\u4ef7\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff0c\u5b83\u5f88\u53ef\u80fd\u4f1a\u88ab\u5206\u4e3a\u4e2d\u7acb\u6216\u51b2\u7a81\uff0c\u56e0\u4e3a\u5b83\u5bf9\u540c\u4e00\u53f0\u5496\u5561\u673a\u6709\u4e24\u79cd\u76f8\u53cd\u7684\u60c5\u611f\u3002<\/p>\n<p><strong>\u53e5\u5b50\u7ea7\u522b<\/strong>\uff1a\u5728\u8fd9\u4e2a\u7ea7\u522b\u5206\u522b\u9884\u6d4b\u6bcf\u4e2a\u53e5\u5b50\u7684\u60c5\u611f\u3002 \u5728\u5496\u5561\u673a\u8bc4\u4ef7\u4e0a\uff0c\u53e5\u5b50\u7ea7\u5206\u6790\u4f1a\u544a\u8bc9\u6211\u4eec\u8bc4\u4ef7\u8005\u5bf9\u4ea7\u54c1\u7684\u67d0\u4e9b\u90e8\u5206\u6301\u6b63\u9762\u6001\u5ea6\uff0c\u4f46\u5bf9\u5176\u4ed6\u90e8\u5206\u6301\u8d1f\u9762\u6001\u5ea6\u3002 \u4e0d\u8fc7\uff0c\u8fd9\u79cd\u5206\u6790\u5e76\u6ca1\u6709\u544a\u8bc9\u6211\u4eec\u8bc4\u4ef7\u8005\u5bf9\u8fd9\u53f0\u5496\u5561\u673a\u559c\u6b22\u548c\u4e0d\u559c\u6b22\u7684\u5730\u65b9\u3002<\/p>\n<p><strong>\u57fa\u4e8e\u5207\u9762<\/strong>\uff1a\u8fd9\u7c7b\u60c5\u611f\u5206\u6790\u6df1\u5165\u63a2\u7a76\u4e00\u6bb5\u6587\u672c\u5e76\u5c1d\u8bd5\u4e86\u89e3\u7528\u6237\u5bf9\u7279\u5b9a\u5207\u9762\u7684\u60c5\u611f\u3002 \u5bf9\u4e8e\u8fd9\u6b3e\u5496\u5561\u673a\u7684\u8bc4\u4ef7\uff0c\u8bc4\u4ef7\u8005\u63d0\u5230\u4e24\u4e2a\u5207\u9762\uff1a<em>\u5916\u89c2<\/em>\u548c<em>\u566a\u97f3<\/em>\u3002 \u63d0\u53d6\u8fd9\u4e9b\u5207\u9762\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u4e00\u6b65\u4e86\u89e3\u7528\u6237\u5177\u4f53\u559c\u6b22\u548c\u4e0d\u559c\u6b22\u4ec0\u4e48\u3002 \u7528\u6237\u5bf9\u673a\u5668\u7684\u5916\u89c2\u6301\u6b63\u9762\u6001\u5ea6\uff0c\u4f46\u5bf9\u673a\u5668\u53d1\u51fa\u7684\u566a\u97f3\u6301\u8d1f\u9762\u6001\u5ea6\u3002<\/p>\n<h4 class=\"wp-block-heading\">\u5c06\u60c5\u611f\u5206\u6790\u4e0e\u5176\u4ed6 NLP \u6280\u672f\u7ed3\u5408<\/h4>\n<p><strong>\u57fa\u4e8e\u610f\u56fe<\/strong>\uff1a\u5728\u6700\u540e\u8fd9\u7c7b\u60c5\u611f\u5206\u6790\u4e2d\uff0c\u6587\u672c\u6309\u4e24\u79cd\u65b9\u5f0f\u5206\u7c7b\uff1a\u8868\u8fbe\u7684\u60c5\u611f\u548c\u6587\u672c\u7684\u4e3b\u9898\u3002 \u4f8b\u5982\uff0c\u5982\u679c\u4e00\u5bb6\u7535\u4fe1\u516c\u53f8\u6536\u5230\u4e00\u5f20\u6295\u8bc9\u5176\u670d\u52a1\u7ecf\u5e38\u4e2d\u65ad\u7684\u5de5\u5355\uff0c\u4ed6\u4eec\u53ef\u4ee5\u5c06\u6587\u672c\u610f\u56fe\u6216\u4e3b\u9898\u5f52\u7c7b\u4e3a<em>\u670d\u52a1\u53ef\u9760\u6027<\/em>\uff0c\u5e76\u5c06\u60c5\u611f\u5f52\u7c7b\u4e3a<em>\u8d1f\u9762<\/em>\u3002 \u4e0e\u57fa\u4e8e\u5207\u9762\u7684\u60c5\u611f\u5206\u6790\u4e00\u6837\uff0c\u9664\u4e86\u5ba2\u6237\u603b\u4f53\u6ee1\u610f\u5ea6\u4e4b\u5916\uff0c\u8fd9\u79cd\u5206\u6790\u8fd8\u53ef\u4ee5\u4e3a\u516c\u53f8\u63d0\u4f9b\u66f4\u591a\u4fe1\u606f\u3002<\/p>\n<h3 class=\"wp-block-heading\">\u60c5\u611f\u5206\u6790\u5e94\u7528<\/h3>\n<p>\u73b0\u5728\uff0c\u60a8\u53ef\u80fd\u5df2\u7ecf\u60f3\u5230\u4e86\u60c5\u611f\u5206\u6790\u7684\u4e00\u4e9b\u6f5c\u5728\u7528\u4f8b\u3002 \u57fa\u672c\u4e0a\uff0c\u5b83\u53ef\u4ee5\u7528\u4e8e\u4efb\u4f55\u53ef\u4ee5\u83b7\u5f97\u6709\u5173\u67d0\u4e2a\u4e3b\u9898\u7684\u6587\u672c\u53cd\u9988\u6216\u610f\u89c1\u7684\u5730\u65b9\u3002 \u7ec4\u7ec7\u6216\u4e2a\u4eba\u53ef\u4ee5\u4f7f\u7528\u60c5\u611f\u5206\u6790\u8fdb\u884c\u793e\u4ea4\u5a92\u4f53\u76d1\u6d4b\uff0c\u4e86\u89e3\u516c\u4f17\u5bf9\u54c1\u724c\u3001\u653f\u5e9c\u5b9e\u4f53\u6216\u4e3b\u9898\u7684\u611f\u53d7\u3002<\/p>\n<p>\u5ba2\u6237\u53cd\u9988\u5206\u6790\u53ef\u4ee5\u7528\u4e8e\u4e86\u89e3\u53cd\u9988\u6216\u5de5\u5355\u4e2d\u8868\u8fbe\u7684\u60c5\u611f\u3002 \u4ea7\u54c1\u8bc4\u4ef7\u5206\u6790\u53ef\u4ee5\u5c55\u793a\u4eba\u4eec\u5bf9\u516c\u53f8\u4ea7\u54c1\u7684\u6ee1\u610f\u6216\u4e0d\u6ee1\u610f\u7a0b\u5ea6\u3002 \u6700\u540e\uff0c\u60c5\u611f\u5206\u6790\u53ef\u4ee5\u6210\u4e3a\u5e02\u573a\u7814\u7a76\u548c\u7ade\u4e89\u5206\u6790\u7684\u4e00\u4e2a\u5173\u952e\u7ec4\u6210\u90e8\u5206\uff0c\u5176\u4e2d\u516c\u4f17\u5bf9\u65b0\u5174\u8d8b\u52bf\u3001\u529f\u80fd\u548c\u7ade\u4e89\u5bf9\u624b\u7684\u611f\u53d7\u53ef\u4ee5\u5e2e\u52a9\u6307\u5bfc\u516c\u53f8\u6218\u7565\u3002<\/p>\n<h2 class=\"wp-block-heading\">\u60c5\u611f\u5206\u6790\u5982\u4f55\u8fd0\u4f5c\uff1f<\/h2>\n<p>\u603b\u4f53\u800c\u8a00\uff0c\u60c5\u611f\u5206\u6790\u662f\u5c06\u5355\u8bcd\uff08\u6216\u8005\uff0c\u5728\u66f4\u590d\u6742\u7684\u6a21\u578b\u4e2d\u4e3a\u6587\u672c\u7684\u6574\u4f53\u57fa\u8c03\uff09\u4e0e\u60c5\u7eea\u8054\u7cfb\u8d77\u6765\u3002 \u6700\u5e38\u89c1\u7684\u60c5\u611f\u5206\u6790\u65b9\u5f0f\u6709\u4ee5\u4e0b\u4e09\u79cd\u3002<\/p>\n<h3 class=\"wp-block-heading\">\u57fa\u4e8e\u8bcd\u5178\u7684\u65b9\u5f0f<\/h3>\n<p>\u8fd9\u4e9b\u65b9\u5f0f\u4f9d\u8d56\u4e8e\u4e00\u4e2a\u8bcd\u5178\uff0c\u5176\u4e2d\u5305\u62ec\u4e00\u7cfb\u5217\u5355\u8bcd\u7684\u60c5\u611f\u5206\u6570\u3002 \u5b83\u4eec\u4f7f\u7528\u4e00\u7ec4\u89c4\u5219\u5c06\u8fd9\u4e9b\u5206\u6570\u7ed3\u5408\uff0c\u83b7\u5f97\u4e00\u6bb5\u6587\u672c\u7684\u6574\u4f53\u60c5\u611f\u3002 \u8fd9\u4e9b\u65b9\u5f0f\u5f80\u5f80\u975e\u5e38\u5feb\uff0c\u8fd8\u5177\u6709\u4ea7\u751f\u66f4\u7ec6\u7c92\u5ea6\u7684\u8fde\u7eed\u60c5\u611f\u5206\u6570\u7684\u4f18\u52bf\u3002 \u4e0d\u8fc7\uff0c\u7531\u4e8e\u8bcd\u5178\u9700\u8981\u624b\u5de5\u5236\u4f5c\uff0c\u5236\u4f5c\u8d77\u6765\u975e\u5e38\u8017\u65f6\u4e14\u6210\u672c\u9ad8\u6602\u3002<\/p>\n<h3 class=\"wp-block-heading\">\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/h3>\n<p>\u8fd9\u4e9b\u65b9\u5f0f\u5728\u5305\u542b\u6587\u672c\u53ca\u5176\u60c5\u611f\u6807\u7b7e\uff08\u4f8b\u5982\u7535\u5f71\u8bc4\u4ef7\uff09\u7684\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff08\u6700\u5e38\u89c1\u7684\u662f\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668\uff09\u3002 \u5728\u6a21\u578b\u4e2d\uff0c\u6587\u672c\u4e00\u822c\u88ab\u5206\u4e3a\u6b63\u9762\u3001\u8d1f\u9762\uff0c\u6709\u65f6\u8fd8\u6709\u4e2d\u7acb\u3002 \u8fd9\u4e9b\u6a21\u578b\u4e5f\u5f80\u5f80\u975e\u5e38\u5feb\uff0c\u4f46\u7531\u4e8e\u5b83\u4eec\u901a\u5e38\u4e0d\u8003\u8651\u8f93\u5165\u4e2d\u5355\u8bcd\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5b83\u4eec\u53ef\u80fd\u96be\u4ee5\u5904\u7406\u6d89\u53ca\u9650\u5b9a\u8bcd\u548c\u5426\u5b9a\u8bcd\u7684\u66f4\u590d\u6742\u7684\u6587\u672c\u3002<\/p>\n<h3 class=\"wp-block-heading\">\u5927\u8bed\u8a00\u6a21\u578b<\/h3>\n<p>\u8fd9\u4e9b\u65b9\u5f0f\u4f9d\u8d56\u4e8e\u5728\u7528\u4e8e\u8bad\u7ec3\u524d\u8ff0\u673a\u5668\u5b66\u4e60\u5206\u7c7b\u5668\u7684\u76f8\u540c\u6570\u636e\u96c6\u4e0a\u5bf9\u9884\u8bad\u7ec3\u7684\u57fa\u4e8e Transformer \u7684\u5927\u8bed\u8a00\u6a21\u578b\u8fdb\u884c\u5fae\u8c03\u3002 \u8fd9\u4e9b\u590d\u6742\u6a21\u578b\u80fd\u591f\u5bf9\u6587\u672c\u4e2d\u5355\u8bcd\u4e4b\u95f4\u7684\u590d\u6742\u5173\u7cfb\u8fdb\u884c\u5efa\u6a21\uff0c\u4f46\u5f80\u5f80\u6bd4\u5176\u4ed6\u4e24\u79cd\u65b9\u5f0f\u6162\u3002<\/p>\n<h2 class=\"wp-block-heading\">Python \u4e2d\u7684\u60c5\u611f\u5206\u6790<\/h2>\n<p><a href=\"https:\/\/www.jetbrains.com.cn\/help\/pycharm\/python.html\" target=\"_blank\" rel=\"noopener\">Python<\/a> \u62e5\u6709\u4e30\u5bcc\u7684 <a href=\"https:\/\/blog.jetbrains.com\/pycharm\/tag\/nlp\/\">NLP<\/a> \u8f6f\u4ef6\u5305\u751f\u6001\u7cfb\u7edf\uff0c\u56e0\u6b64\u5728\u4f7f\u7528\u8fd9\u79cd\u8bed\u8a00\u8fdb\u884c\u60c5\u611f\u5206\u6790\u65f6\u4f1a\u6709\u5f88\u591a\u9009\u62e9\u3002<\/p>\n<p>\u6211\u4eec\u6765\u770b\u4e00\u4e9b\u6700\u6d41\u884c\u7684\u60c5\u611f\u5206\u6790 <a href=\"https:\/\/www.jetbrains.com.cn\/guide\/python\/tutorials\/getting-started-pycharm\/installing-and-managing-python-packages\/\" target=\"_blank\" rel=\"noopener\">Python \u8f6f\u4ef6\u5305<\/a>\u3002<\/p>\n<h3 class=\"wp-block-heading\">\u7528\u4e8e\u60c5\u611f\u5206\u6790\u7684\u6700\u4f73 Python \u5e93<\/h3>\n<h4 class=\"wp-block-heading\">VADER<\/h4>\n<p><a href=\"https:\/\/www.nltk.org\/api\/nltk.sentiment.vader.html\" target=\"_blank\" rel=\"noopener\">VADER\uff08\u6548\u4ef7\u611f\u77e5\u8bcd\u5178\u548c\u60c5\u611f\u63a8\u7406\u5668\uff09<\/a>\u662f\u4e00\u79cd\u6d41\u884c\u7684\u57fa\u4e8e\u8bcd\u5178\u7684\u60c5\u611f\u5206\u6790\u5668\u3002 \u8fd9\u4e2a\u5206\u6790\u5668\u5185\u7f6e\u4e8e\u5f3a\u5927\u7684 <a href=\"https:\/\/www.nltk.org\/index.html\" target=\"_blank\" rel=\"noopener\">NLTK \u8f6f\u4ef6\u5305<\/a>\u4e2d\uff0c\u8fd4\u56de\u56db\u79cd\u60c5\u611f\u5206\u6570\uff1a\u6587\u672c\u7684<em>\u6b63\u9762<\/em>\u3001<em>\u4e2d\u7acb<\/em>\u6216<em>\u8d1f\u9762<\/em>\u7684\u7a0b\u5ea6\uff0c\u4ee5\u53ca<em>\u590d\u5408<\/em>\u60c5\u611f\u5206\u6570\u3002 \u6b63\u9762\u3001\u4e2d\u7acb\u548c\u8d1f\u9762\u5206\u6570\u7684\u8303\u56f4\u662f 0 \u5230 1\uff0c\u8868\u793a\u6b63\u9762\u3001\u4e2d\u7acb\u6216\u8d1f\u9762\u6587\u672c\u7684\u6bd4\u4f8b\u3002 \u590d\u5408\u5206\u6570\u7684\u8303\u56f4\u662f -1\uff08\u6781\u5176\u8d1f\u9762\uff09\u5230 1\uff08\u6781\u5176\u6b63\u9762\uff09\uff0c\u8868\u793a\u6587\u672c\u7684\u6574\u4f53\u60c5\u611f\u6548\u4ef7\u3002<\/p>\n<p>\u6211\u4eec\u6765\u770b\u4e00\u4e2a\u5c55\u793a\u5176\u8fd0\u4f5c\u65b9\u5f0f\u7684\u57fa\u672c\u793a\u4f8b\uff1a<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">from nltk.sentiment.vader import SentimentIntensityAnalyzer\nimport nltk<\/pre>\n<p>\u6211\u4eec\u9996\u5148\u9700\u8981\u4e0b\u8f7d VADER \u8bcd\u5178\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">nltk.download('vader_lexicon')<\/pre>\n<p>\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9e\u4f8b\u5316 VADER <code>SentimentIntensityAnalyzer()<\/code> \u5e76\u4f7f\u7528 <code>polarity_scores()<\/code> \u65b9\u6cd5\u63d0\u53d6\u60c5\u611f\u5206\u6570\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">analyzer = SentimentIntensityAnalyzer()\n\nsentence = \"I love PyCharm! It's my favorite Python IDE.\"\nsentiment_scores = analyzer.polarity_scores(sentence)\nprint(sentiment_scores)<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"raw\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">{'neg': 0.0, 'neu': 0.572, 'pos': 0.428, 'compound': 0.6696}<\/pre>\n<p>\u53ef\u4ee5\u770b\u5230\uff0cVADER \u7ed9\u8fd9\u6bb5\u6587\u672c\u7684\u603b\u4f53\u60c5\u611f\u5206\u6570\u4e3a 0.67\uff0c\u5c06\u5176\u5185\u5bb9\u5206\u7c7b\u4e3a 43% \u6b63\u9762\u300157% \u4e2d\u7acb\u548c 0% \u8d1f\u9762\u3002<\/p>\n<p>VADER \u7684\u8fd0\u4f5c\u65b9\u5f0f\u662f\u5728\u8bcd\u5178\u4e2d\u67e5\u627e\u6bcf\u4e2a\u5355\u8bcd\u7684\u60c5\u611f\u5206\u6570\uff0c\u7136\u540e\u4f7f\u7528\u4e00\u7ec4\u7ec6\u81f4\u89c4\u5219\u5c06\u5b83\u4eec\u7ec4\u5408\u8d77\u6765\u3002 \u4f8b\u5982\uff0c\u9650\u5b9a\u8bcd\u53ef\u4ee5\u589e\u52a0\u6216\u51cf\u5c11\u5355\u8bcd\u60c5\u611f\u7684\u5f3a\u5ea6\uff0c\u56e0\u6b64\u5355\u8bcd\u524d\u7684\u9650\u5b9a\u8bcd\uff08\u5982\u201ca bit\u201d\uff09\u4f1a\u964d\u4f4e\u60c5\u611f\u5f3a\u5ea6\uff0c\u800c\u201cextremely\u201d\u5219\u4f1a\u589e\u5f3a\u60c5\u611f\u5f3a\u5ea6\u3002<\/p>\n<p>VADER \u7684\u8bcd\u5178\u5305\u62ec\u201csmh\u201d\uff08shaking my head\uff0c\u6447\u5934\uff09\u7b49\u7f29\u5199\u548c\u8868\u60c5\u7b26\u53f7\uff0c\u7279\u522b\u9002\u5408\u793e\u4ea4\u5a92\u4f53\u6587\u672c\u3002 VADER \u7684\u4e3b\u8981\u9650\u5236\u662f\u5b83\u4e0d\u9002\u7528\u4e8e\u82f1\u8bed\u4ee5\u5916\u7684\u8bed\u8a00\uff0c\u4f46\u60a8\u53ef\u4ee5\u4f7f\u7528 <a href=\"https:\/\/github.com\/brunneis\/vader-multi\" target=\"_blank\" rel=\"noopener\"><code>vader-multi<\/code><\/a> \u7b49\u9879\u76ee\u66ff\u4ee3\u3002 \u5982\u679c\u60a8\u6709\u5174\u8da3\u6df1\u5165\u4e86\u89e3\u8fd9\u4e2a\u8f6f\u4ef6\u5305\uff0c\u6211\u5199\u4e86<a href=\"https:\/\/t-redactyl.io\/blog\/2017\/04\/using-vader-to-handle-sentiment-analysis-with-social-media-text.html\" target=\"_blank\" rel=\"noopener\">\u4e00\u7bc7\u6709\u5173 VADER \u8fd0\u4f5c\u65b9\u5f0f\u7684\u6587\u7ae0<\/a>\u3002<\/p>\n<h4 class=\"wp-block-heading\">NLTK<\/h4>\n<p>\u6b64\u5916\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528 NLTK \u8bad\u7ec3\u81ea\u5df1\u7684\u673a\u5668\u5b66\u4e60\u60c5\u611f\u5206\u7c7b\u5668\uff0c\u4f7f\u7528\u6765\u81ea <code>scikit-learn<\/code> \u7684\u5206\u7c7b\u5668\u3002<\/p>\n<p>\u60a8\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5904\u7406\u6587\u672c\u5e76\u9988\u5165\u8fd9\u4e9b\u6a21\u578b\uff0c\u4f46\u6700\u7b80\u5355\u7684\u65b9\u5f0f\u662f\u57fa\u4e8e\u6587\u672c\u4e2d\u5b58\u5728\u7684\u5355\u8bcd\u8fdb\u884c\u5904\u7406\uff0c\u8fd9\u79cd\u6587\u672c\u5efa\u6a21\u79f0\u4e3a\u8bcd\u888b\u65b9\u5f0f\u3002 \u6700\u76f4\u63a5\u7684\u8bcd\u888b\u5efa\u6a21\u7c7b\u578b\u662f<em>\u4e8c\u5143\u77e2\u91cf\u5316<\/em>\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5355\u8bcd\u88ab\u89c6\u4e3a\u4e00\u4e2a\u7279\u5f81\uff0c\u8be5\u7279\u5f81\u7684\u503c\u4e3a 0 \u6216 1\uff08\u5206\u522b\u8868\u793a\u8be5\u5355\u8bcd\u5728\u6587\u672c\u4e2d\u662f\u5426\u5b58\u5728\uff09\u3002<\/p>\n<p>\u5982\u679c\u60a8\u521a\u63a5\u89e6\u6587\u672c\u6570\u636e\u548c NLP\uff0c\u60f3\u8981\u8be6\u7ec6\u4e86\u89e3\u5982\u4f55\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u673a\u5668\u5b66\u4e60\u6a21\u578b\u7684\u8f93\u5165\uff0c\u6211\u5728\u4e00\u6bb5<a href=\"https:\/\/www.youtube.com\/live\/WYmyZBg2VFI?feature=shared&amp;t=261\" target=\"_blank\" rel=\"noopener\">\u4e3b\u9898\u6f14\u8bb2<\/a>\u4e2d\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u4ecb\u7ecd\u3002<\/p>\n<p><a href=\"https:\/\/www.nltk.org\/howto\/sentiment.html#sentiment-analysis\" target=\"_blank\" rel=\"noopener\">NLTK \u6587\u6863<\/a>\u5c55\u793a\u4e86\u4e00\u4e2a\u793a\u4f8b\uff0c\u5176\u4e2d\u4e00\u4e2a\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668\u88ab\u8bad\u7ec3\u6765\u9884\u6d4b\u4e00\u6bb5\u6587\u672c\u662f\u4e3b\u89c2\u8fd8\u662f\u5ba2\u89c2\u3002 \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u4ed6\u4eec\u6839\u636e\u89c4\u5219\u4e3a\u4e00\u4e9b\u672f\u8bed\u6dfb\u52a0\u4e86\u989d\u5916\u7684\u5426\u5b9a\u9650\u5b9a\u8bcd\uff0c\u8fd9\u4e9b\u89c4\u5219\u8868\u660e\u8be5\u8bcd\u6216\u5b57\u7b26\u662f\u5426\u53ef\u80fd\u6d89\u53ca\u5426\u5b9a\u6587\u672c\u5176\u4ed6\u5730\u65b9\u8868\u8fbe\u7684\u60c5\u611f\u3002 \u5982\u679c\u60a8\u60f3\u8be6\u7ec6\u4e86\u89e3\u8fd9\u4e2a\u4e3b\u9898\uff0cReal Python \u4e5f\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5173\u4e8e\u4f7f\u7528 NLTK \u81ea\u884c\u8bad\u7ec3\u5206\u7c7b\u5668\u7684<a href=\"https:\/\/realpython.com\/python-nltk-sentiment-analysis\/#customizing-nltks-sentiment-analysis\" target=\"_blank\" rel=\"noopener\">\u60c5\u611f\u5206\u6790\u6559\u7a0b<\/a>\u3002<\/p>\n<h4 class=\"wp-block-heading\">Pattern \u548c TextBlob<\/h4>\n<p><a href=\"https:\/\/github.com\/clips\/pattern\" target=\"_blank\" rel=\"noopener\">Pattern<\/a> \u8f6f\u4ef6\u5305\u63d0\u4f9b\u4e86\u53e6\u4e00\u79cd\u57fa\u4e8e\u8bcd\u5178\u7684<a href=\"https:\/\/github.com\/clips\/pattern\/blob\/d25511f9ca7ed9356b801d8663b8b5168464e68f\/pattern\/text\/__init__.py#L2316\" target=\"_blank\" rel=\"noopener\">\u60c5\u611f\u5206\u6790<\/a>\u65b9\u5f0f\u3002 \u5b83\u4f7f\u7528 <a href=\"https:\/\/github.com\/aesuli\/SentiWordNet\" target=\"_blank\" rel=\"noopener\">SentiWordNet<\/a> \u8bcd\u5178\uff0c\u5176\u4e2d <a href=\"https:\/\/github.com\/clips\/pattern\" target=\"_blank\" rel=\"noopener\">WordNet<\/a> \u4e2d\u7684\u6bcf\u4e2a\u540c\u4e49\u8bcd\u7ec4 (<em>synset<\/em>) \u90fd\u88ab\u6307\u5b9a\u4e00\u4e2a\u6b63\u9762\u3001\u8d1f\u9762\u548c\u5ba2\u89c2\u6027\u7684\u5206\u6570\u3002 \u6bcf\u4e2a\u5355\u8bcd\u7684\u6b63\u8d1f\u5206\u6570\u901a\u8fc7\u4e00\u7cfb\u5217\u89c4\u5219\u5408\u5e76\uff0c\u5f97\u51fa\u6700\u7ec8\u7684\u6781\u6027\u5206\u6570\u3002 \u540c\u6837\uff0c\u6bcf\u4e2a\u5355\u8bcd\u7684\u5ba2\u89c2\u5206\u6570\u5408\u5e76\uff0c\u5f97\u51fa\u6700\u7ec8\u7684\u4e3b\u89c2\u5206\u6570\u3002<\/p>\n<p>\u7531\u4e8e WordNet \u5305\u542b\u8bcd\u6027\u4fe1\u606f\uff0c\u89c4\u5219\u53ef\u4ee5\u8003\u8651\u5355\u8bcd\u524d\u9762\u7684\u5f62\u5bb9\u8bcd\u6216\u526f\u8bcd\u662f\u5426\u4f1a\u6539\u53d8\u5176\u60c5\u611f\u3002 \u89c4\u5219\u96c6\u8fd8\u8003\u8651\u4e86\u5426\u5b9a\u3001\u611f\u53f9\u53f7\u548c\u8868\u60c5\u7b26\u53f7\uff0c\u751a\u81f3\u5305\u62ec\u4e00\u4e9b\u5904\u7406\u6210\u8bed\u548c\u8bbd\u523a\u7684\u89c4\u5219\u3002<\/p>\n<p>\u4e0d\u8fc7\uff0cPattern \u4f5c\u4e3a\u72ec\u7acb\u5e93\u4ec5\u4e0e Python 3.6 \u517c\u5bb9\u3002 \u56e0\u6b64\uff0c\u4f7f\u7528 Pattern \u7684\u6700\u5e38\u89c1\u65b9\u5f0f\u662f\u901a\u8fc7 <a href=\"https:\/\/textblob.readthedocs.io\/en\/dev\/\" target=\"_blank\" rel=\"noopener\">TextBlob<\/a>\u3002 <a href=\"https:\/\/github.com\/sloria\/TextBlob\/blob\/e19171014bfba910d1e33527f46d514837da234e\/src\/textblob\/en\/sentiments.py#L15\" target=\"_blank\" rel=\"noopener\">TextBlob \u60c5\u611f\u5206\u6790\u5668<\/a>\u9ed8\u8ba4\u4f7f\u7528\u81ea\u5df1\u7684 Pattern \u5e93\u5b9e\u73b0\u6765\u751f\u6210\u60c5\u611f\u5206\u6570\u3002<\/p>\n<p>\u6211\u4eec\u6765\u770b\u770b\u5b83\u7684\u5b9e\u9645\u8fd0\u4f5c\uff1a<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">from textblob import TextBlob<\/pre>\n<p>\u60a8\u53ef\u4ee5\u770b\u5230\u6211\u4eec\u5728\u6587\u672c\u4e0a\u8fd0\u884c TextBlob \u65b9\u6cd5\uff0c\u7136\u540e\u4f7f\u7528 <code>sentiment<\/code> \u7279\u6027\u63d0\u53d6\u60c5\u611f\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">pattern_blob = TextBlob(\"I love PyCharm! It's my favorite Python IDE.\")\nsentiment = pattern_blob.sentiment\n\nprint(f\"Polarity: {sentiment.polarity}\")\nprint(f\"Subjectivity: {sentiment.subjectivity}\")<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"raw\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">Polarity: 0.625\nSubjectivity: 0.6<\/pre>\n<p>\u5bf9\u4e8e\u6211\u4eec\u7684\u4f8b\u53e5\uff0cTextBlob \u4e2d\u7684 Pattern \u7ed9\u51fa\u4e86\u6781\u6027\u5206\u6570 0.625\uff08\u76f8\u5bf9\u63a5\u8fd1 VADER \u7ed9\u51fa\u7684\u5206\u6570\uff09\u548c\u4e3b\u89c2\u5206\u6570 0.6\u3002<\/p>\n<p>\u4f46\u5728 TextBlob \u4e2d\u8fd8\u6709\u7b2c\u4e8c\u79cd\u83b7\u53d6\u60c5\u611f\u5206\u6570\u7684\u65b9\u5f0f\u3002 \u8fd9\u4e2a\u8f6f\u4ef6\u5305\u8fd8\u5305\u62ec\u4e00\u4e2a<a href=\"https:\/\/github.com\/sloria\/TextBlob\/blob\/e19171014bfba910d1e33527f46d514837da234e\/src\/textblob\/en\/sentiments.py#L53\" target=\"_blank\" rel=\"noopener\">\u9884\u8bad\u7ec3\u7684\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668<\/a>\uff0c\u5b83\u4f1a\u5c06\u4e00\u6bb5\u6587\u672c\u6807\u8bb0\u4e3a\u6b63\u9762\u6216\u8d1f\u9762\uff0c\u5e76\u7ed9\u51fa\u6587\u672c\u4e3a\u6b63\u9762\u6216\u8d1f\u9762\u7684\u6982\u7387\u3002<\/p>\n<p>\u8981\u4f7f\u7528\u6b64\u65b9\u5f0f\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u4ece NLTK \u4e0b\u8f7d <code>punkt<\/code> \u6a21\u5757\u548c <code>movie-reviews<\/code> \u6570\u636e\u96c6\uff0c\u7528\u4e8e\u8bad\u7ec3\u6b64\u6a21\u578b\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import nltk\nnltk.download('movie_reviews')\nnltk.download('punkt')\n\nfrom textblob import TextBlob\nfrom textblob.sentiments import NaiveBayesAnalyzer<\/pre>\n<p>\u540c\u6837\uff0c\u6211\u4eec\u9700\u8981\u5728\u6587\u672c\u4e0a\u8fd0\u884c <code>TextBlob<\/code>\uff0c\u4f46\u8fd9\u6b21\u6211\u4eec\u6dfb\u52a0\u5b9e\u53c2 <code>analyzer=NaiveBayesAnalyzer()<\/code>\u3002 \u7136\u540e\uff0c\u548c\u5148\u524d\u4e00\u6837\uff0c\u6211\u4eec\u4f7f\u7528 sentiment \u7279\u6027\u63d0\u53d6\u60c5\u611f\u5206\u6570\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">nb_blob = TextBlob(\"I love PyCharm! It's my favorite Python IDE.\", analyzer=NaiveBayesAnalyzer())\nsentiment = nb_blob.sentiment\nprint(sentiment)<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"raw\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">Sentiment(classification='pos', p_pos=0.5851800554016624, p_neg=0.4148199445983381)<\/pre>\n<p>\u8fd9\u6b21\u6211\u4eec\u5f97\u5230\u4e00\u4e2a <code>pos<\/code>\uff08positive\uff0c\u6b63\u9762\uff09\u6807\u7b7e\uff0c\u6a21\u578b\u9884\u6d4b\u8be5\u6587\u672c\u4e3a\u6b63\u9762\u7684\u6982\u7387\u4e3a 59%\uff0c\u4e3a\u8d1f\u9762\u7684\u6982\u7387\u4e3a 41%\u3002<\/p>\n<h4 class=\"wp-block-heading\">spaCy<\/h4>\n<p>\u53e6\u4e00\u79cd\u9009\u62e9\u662f\u4f7f\u7528 <a href=\"https:\/\/spacy.io\/\" target=\"_blank\" rel=\"noopener\">spaCy<\/a> \u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002 spaCy \u662f Python \u4e2d\u7684\u53e6\u4e00\u4e2a\u6d41\u884c NLP \u8f6f\u4ef6\u5305\uff0c\u5e76\u4e14\u5177\u6709\u5e7f\u6cdb\u7684\u6587\u672c\u5904\u7406\u9009\u9879\u3002<\/p>\n<p>\u7b2c\u4e00\u79cd\u65b9\u5f0f\u662f\u4f7f\u7528 <a href=\"https:\/\/spacy.io\/universe\/project\/spacy-textblob\" target=\"_blank\" rel=\"noopener\">spacytextblob<\/a> \u63d2\u4ef6\u5c06 TextBlob \u60c5\u611f\u5206\u6790\u5668\u4f5c\u4e3a spaCy \u7ba1\u9053\u7684\u4e00\u90e8\u5206\u3002 \u5728\u6267\u884c\u6b64\u64cd\u4f5c\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5 <code>spacy<\/code> \u548c <code>spacytextblob<\/code> \u5e76\u4e0b\u8f7d\u5408\u9002\u7684\u8bed\u8a00\u6a21\u578b\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import spacy\nimport spacy.cli\nfrom spacytextblob.spacytextblob import SpacyTextBlob\n\nspacy.cli.download(\"en_core_web_sm\")<\/pre>\n<p>\u7136\u540e\uff0c\u52a0\u8f7d\u6b64\u8bed\u8a00\u6a21\u578b\u5e76\u5c06 <code>spacytextblob<\/code> \u6dfb\u52a0\u5230\u6587\u672c\u5904\u7406\u7ba1\u9053\u4e2d\u3002 TextBlob \u53ef\u4ee5\u901a\u8fc7 spaCy \u7684 <code>pipe<\/code> \u65b9\u6cd5\u4f7f\u7528\uff0c\u8fd9\u610f\u5473\u7740\u6211\u4eec\u53ef\u4ee5\u5c06\u5176\u4f5c\u4e3a\u66f4\u590d\u6742\u7684\u6587\u672c\u5904\u7406\u7ba1\u9053\u7684\u4e00\u90e8\u5206\uff0c\u5305\u62ec\u8bcd\u6027\u6807\u6ce8\u3001\u8bcd\u5f62\u8fd8\u539f\u548c\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u7b49\u9884\u5904\u7406\u6b65\u9aa4\u3002 \u9884\u5904\u7406\u53ef\u4ee5\u89c4\u8303\u548c\u4e30\u5bcc\u6587\u672c\uff0c\u5e2e\u52a9\u4e0b\u6e38\u6a21\u578b\u4ece\u6587\u672c\u8f93\u5165\u4e2d\u83b7\u53d6\u6700\u591a\u4fe1\u606f\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">nlp = spacy.load('en_core_web_sm')\nnlp.add_pipe('spacytextblob')<\/pre>\n<p>\u73b0\u5728\uff0c\u6211\u4eec\u5c06\u53ea\u5206\u6790\u4f8b\u53e5\uff0c\u4e0d\u8fdb\u884c\u9884\u5904\u7406\uff1a<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">doc = nlp(\"I love PyCharm! It's my favorite Python IDE.\")\n\nprint('Polarity: ', doc._.polarity)\nprint('Subjectivity: ', doc._.subjectivity)<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"raw\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">Polarity:  0.625\nSubjectivity:  0.6<\/pre>\n<p>\u6211\u4eec\u5f97\u5230\u7684\u7ed3\u679c\u4e0e\u4e0a\u9762\u4f7f\u7528 TextBlob \u65f6\u76f8\u540c\u3002<\/p>\n<p>\u5728 spaCy \u4e2d\u8fdb\u884c\u60c5\u611f\u5206\u6790\u7684\u7b2c\u4e8c\u79cd\u65b9\u5f0f\u662f\u4f7f\u7528 <a href=\"https:\/\/spacy.io\/api\/textcategorizer\" target=\"_blank\" rel=\"noopener\">TextCategorizer \u7c7b<\/a>\u8bad\u7ec3\u81ea\u5df1\u7684\u6a21\u578b\u3002 \u8fd9\u6837\u4e00\u6765\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u60c5\u611f\u5206\u6790\u8bad\u7ec3\u96c6\u8bad\u7ec3\u4e00\u7cfb\u5217 <a href=\"https:\/\/spacy.io\/api\/architectures\" target=\"_blank\" rel=\"noopener\">spaCY \u521b\u5efa\u7684\u6a21\u578b<\/a>\u3002 \u540c\u6837\uff0c\u7531\u4e8e\u8fd9\u53ef\u4ee5\u7528\u4f5c spaCy \u7ba1\u9053\u7684\u4e00\u90e8\u5206\uff0c\u5728\u8bad\u7ec3\u6a21\u578b\u4e4b\u524d\uff0c\u60a8\u6709\u8bb8\u591a\u9884\u5904\u7406\u6587\u672c\u7684\u9009\u62e9\u3002<\/p>\n<p>\u6700\u540e\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u5927\u8bed\u8a00\u6a21\u578b\u901a\u8fc7 <a href=\"https:\/\/spacy.io\/api\/large-language-models#sentiment\" target=\"_blank\" rel=\"noopener\">spacy-llm<\/a> \u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002 \u8fd9\u6837\u4e00\u6765\uff0c\u60a8\u53ef\u4ee5\u63d0\u793a\u6765\u81ea OpenAI\u3001Anthropic\u3001Cohere \u548c Google \u7684\u5404\u79cd\u4e13\u6709\u5927\u8bed\u8a00\u6a21\u578b (LLM) \u5bf9\u6587\u672c\u6267\u884c\u60c5\u611f\u5206\u6790\u3002<\/p>\n<p>\u8fd9\u79cd\u65b9\u5f0f\u4e0e\u6211\u4eec\u8ba8\u8bba\u8fc7\u7684\u5176\u4ed6\u65b9\u5f0f\u7565\u6709\u4e0d\u540c\u3002 \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 GPT-4 \u8fd9\u6837\u7684\u901a\u7528\u6a21\u578b\u6765\u9884\u6d4b\u6587\u672c\u7684\u60c5\u611f\uff0c\u800c\u65e0\u9700\u8bad\u7ec3\u6a21\u578b\u3002 \u4e3a\u6b64\uff0c\u60a8\u53ef\u4ee5\u9009\u62e9\u96f6\u6837\u672c\u5b66\u4e60\uff08\u63d0\u793a\u88ab\u4f20\u9012\u5230\u6a21\u578b\u4f46\u6ca1\u6709\u793a\u4f8b\uff09\u6216\u5c11\u6837\u672c\u5b66\u4e60\uff08\u63d0\u793a\u548c\u5c11\u8bb8\u793a\u4f8b\u88ab\u4f20\u9012\u5230\u6a21\u578b\uff09\u3002<\/p>\n<h4 class=\"wp-block-heading\">Transformers<\/h4>\n<p>\u6211\u4eec\u5c06\u8ba8\u8bba\u7684\u7528\u4e8e\u60c5\u611f\u5206\u6790\u7684\u6700\u540e\u4e00\u4e2a Python \u8f6f\u4ef6\u5305\u662f <a href=\"https:\/\/huggingface.co\/\" target=\"_blank\" rel=\"noopener\">Hugging Face<\/a> \u7684 <a href=\"https:\/\/huggingface.co\/docs\/transformers\/en\/index\" target=\"_blank\" rel=\"noopener\">Transformers<\/a>\u3002<\/p>\n<p>Hugging Face \u6258\u7ba1\u6240\u6709\u4e3b\u8981\u5f00\u6e90 LLM \u4f9b\u514d\u8d39\u4f7f\u7528\uff08\u4ee5\u53ca\u5176\u4ed6\u6a21\u578b\uff0c\u5305\u62ec\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u97f3\u9891\u6a21\u578b\uff09\uff0c\u5e76\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7528\u4e8e\u8bad\u7ec3\u3001\u90e8\u7f72\u548c\u5171\u4eab\u8fd9\u4e9b\u6a21\u578b\u7684\u5e73\u53f0\u3002 \u5b83\u7684 Transformers \u8f6f\u4ef6\u5305\u63d0\u4f9b\u4e86\u5e7f\u6cdb\u7684\u529f\u80fd\uff08\u5305\u62ec\u60c5\u611f\u5206\u6790\uff09\uff0c\u53ef\u4e0e Hugging Face \u6258\u7ba1\u7684 LLM \u914d\u5408\u4f7f\u7528\u3002<\/p>\n<h2 class=\"wp-block-heading\">\u7406\u89e3\u60c5\u611f\u5206\u6790\u5668\u7684\u7ed3\u679c<\/h2>\n<p>\u73b0\u5728\uff0c\u6211\u4eec\u5df2\u7ecf\u4ecb\u7ecd\u8fc7\u4f7f\u7528 Python \u8fdb\u884c\u60c5\u611f\u5206\u6790\u7684\u6240\u6709\u65b9\u5f0f\uff0c\u60a8\u53ef\u80fd\u4f1a\u60f3\uff1a\u201c\u6211\u600e\u6837\u624d\u80fd\u628a\u5b83\u5e94\u7528\u5230\u81ea\u5df1\u7684\u6570\u636e\u5462\uff1f\u201d<\/p>\n<p>\u4e3a\u4e86\u7406\u89e3\u8fd9\u4e00\u70b9\uff0c\u6211\u4eec\u4f7f\u7528 PyCharm \u6bd4\u8f83 VADER \u548c TextBlob \u8fd9\u4e24\u4e2a\u8f6f\u4ef6\u5305\u3002 \u5b83\u4eec\u7684\u591a\u79cd\u60c5\u611f\u5206\u6570\u4e3a\u6211\u4eec\u7684\u6570\u636e\u63d0\u4f9b\u4e86\u4e00\u4e9b\u4e0d\u540c\u7684\u89c6\u89d2\u3002 \u6211\u4eec\u5c06\u4f7f\u7528\u8fd9\u4e9b\u8f6f\u4ef6\u5305\u5206\u6790 Amazon \u8bc4\u4ef7\u6570\u636e\u96c6\u3002<\/p>\n<p>PyCharm Professional \u662f\u4e00\u6b3e\u7528\u4e8e<a href=\"https:\/\/www.jetbrains.com.cn\/pycharm\/data-science\/\" target=\"_blank\" rel=\"noopener\">\u6570\u636e\u79d1\u5b66<\/a>\u7684\u5f3a\u5927 Python IDE\uff0c\u652f\u6301\u9ad8\u7ea7 Python <a href=\"https:\/\/www.jetbrains.com.cn\/help\/pycharm\/auto-completing-code.html\" target=\"_blank\" rel=\"noopener\">\u4ee3\u7801\u8865\u5168<\/a>\u3001\u68c0\u67e5\u548c<a href=\"https:\/\/www.jetbrains.com.cn\/help\/pycharm\/debugging-code.html\" target=\"_blank\" rel=\"noopener\">\u8c03\u8bd5<\/a>\u3001\u4e30\u5bcc\u7684<a href=\"https:\/\/www.jetbrains.com.cn\/pycharm\/integrations\/#databases\" target=\"_blank\" rel=\"noopener\">\u6570\u636e\u5e93<\/a>\u3001<a href=\"https:\/\/www.jetbrains.com.cn\/help\/pycharm\/running-jupyter-notebook-cells.html\" target=\"_blank\" rel=\"noopener\">Jupyter<\/a>\u3001<a href=\"https:\/\/www.jetbrains.com.cn\/help\/pycharm\/using-git-integration.html\" target=\"_blank\" rel=\"noopener\">Git<\/a>\u3001<a href=\"https:\/\/www.jetbrains.com.cn\/help\/pycharm\/conda-support-creating-conda-virtual-environment.html\" target=\"_blank\" rel=\"noopener\">Conda<\/a> \u7b49 \u2013 \u5168\u90e8\u5f00\u7bb1\u5373\u7528\u3002 \u6b64\u5916\uff0c\u60a8\u8fd8\u5c06\u83b7\u5f97\u975e\u5e38\u6709\u7528\u7684\u529f\u80fd\uff0c\u4f8b\u5982\u6211\u4eec\u7684 DataFrame <em>Column Statistics<\/em>\uff08\u5217\u7edf\u8ba1\u4fe1\u606f\uff09\u548c <em>Chart View<\/em>\uff08\u56fe\u8868\u89c6\u56fe\uff09\uff0c\u4ee5\u53ca Hugging Face <a href=\"https:\/\/www.jetbrains.com.cn\/pycharm\/integrations\/\" target=\"_blank\" rel=\"noopener\">\u96c6\u6210<\/a>\uff0c\u4ece\u800c\u66f4\u5feb\u66f4\u8f7b\u677e\u5730\u4f7f\u7528 LLM\u3002 \u5728\u8fd9\u7bc7\u535a\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u63a2\u7d22 PyCharm \u5904\u7406 DataFrame \u7684\u9ad8\u7ea7\u529f\u80fd\uff0c\u7531\u6b64\u5feb\u901f\u4e86\u89e3\u200b\u200b\u60c5\u611f\u5206\u6570\u5728\u4e24\u4e2a\u8f6f\u4ef6\u5305\u4e4b\u95f4\u7684\u5206\u5e03\u3002<\/p>\n<p>\u5982\u679c\u60a8\u73b0\u5728\u6253\u7b97\u5f00\u59cb\u81ea\u5df1\u7684\u60c5\u611f\u5206\u6790\u9879\u76ee\uff0c\u60a8\u53ef\u4ee5\u6fc0\u6d3b PyCharm \u7684\u4e09\u4e2a\u6708\u514d\u8d39\u8ba2\u9605\u3002 \u70b9\u51fb\u4e0b\u65b9\u94fe\u63a5\uff0c\u8f93\u5165\u6b64\u4fc3\u9500\u4ee3\u7801\uff1a<strong>PCSA24<\/strong>\u3002 \u7136\u540e\uff0c\u60a8\u5c06\u901a\u8fc7\u7535\u5b50\u90ae\u4ef6\u6536\u5230\u6fc0\u6d3b\u7801\u3002<\/p>\n<div class=\"buttons\">\n<div class=\"buttons__row\"><a class=\"btn\" href=\"https:\/\/www.jetbrains.com.cn\/store\/redeem\/\" target=\"\" rel=\"noopener\">\u6fc0\u6d3b 3 \u4e2a\u6708\u8ba2\u9605<\/a><\/div>\n<\/div>\n<p>\u6211\u4eec\u9996\u5148\u9700\u8981\u52a0\u8f7d\u6570\u636e\u3002 \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 Datasets \u8f6f\u4ef6\u5305\u4e2d\u7684 <code>load_dataset()<\/code> \u65b9\u6cd5<a href=\"https:\/\/huggingface.co\/datasets\/fancyzhx\/amazon_polarity\" target=\"_blank\" rel=\"noopener\">\u4ece Hugging Face Hub \u4e0b\u8f7d\u8fd9\u4e9b\u6570\u636e<\/a>\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">from datasets import load_dataset\namazon = load_dataset(\"fancyzhx\/amazon_polarity\")<\/pre>\n<p>\u5c06\u9f20\u6807\u60ac\u505c\u5728\u6570\u636e\u96c6\u540d\u79f0\u4e0a\u5373\u53ef\u5728 PyCharm \u5185\u67e5\u770b Hugging Face \u6570\u636e\u96c6\u5361\uff0c\u8fd9\u6837\u4e00\u6765\uff0c\u65e0\u9700\u79bb\u5f00 IDE \u5373\u53ef\u8f7b\u677e\u83b7\u53d6\u6709\u5173 Hugging Face \u8d44\u4ea7\u7684\u4fe1\u606f\u3002<\/p>\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532334\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/11\/Screenshot-2024-11-29-at-16.59.07.png\" alt=\"\" width=\"2354\" height=\"1372\" \/><\/figure>\n<p>\u6211\u4eec\u5728\u8fd9\u91cc\u53ef\u4ee5\u770b\u5230\u6b64\u6570\u636e\u96c6\u7684\u5185\u5bb9\uff1a<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">amazon<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"raw\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">DatasetDict({\n    train: Dataset({\n        features: ['label', 'title', 'content'],\n        num_rows: 3600000\n    })\n    test: Dataset({\n        features: ['label', 'title', 'content'],\n        num_rows: 400000\n    })\n})<\/pre>\n<p>\u8bad\u7ec3\u6570\u636e\u96c6\u6709 360 \u4e07\u4e2a\u89c2\u6d4b\uff0c\u6d4b\u8bd5\u6570\u636e\u96c6\u5305\u542b 400,000 \u4e2a\u3002 \u5728\u672c\u6559\u7a0b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u8bad\u7ec3\u6570\u636e\u96c6\u3002<\/p>\n<p>\u73b0\u5728\uff0c\u52a0\u8f7d VADER <code>SentimentIntensityAnalyzer<\/code> \u548c TextBlob \u65b9\u6cd5\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">from nltk.sentiment.vader import SentimentIntensityAnalyzer\nimport nltk\n\nnltk.download(\"vader_lexicon\")\n\nanalyzer = SentimentIntensityAnalyzer()<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">from textblob import TextBlob<\/pre>\n<p>\u8bad\u7ec3\u6570\u636e\u96c6\u5305\u542b\u592a\u591a\u89c2\u6d4b\uff0c\u96be\u4ee5\u8f7b\u677e\u76f4\u89c2\u5448\u73b0\uff0c\u56e0\u6b64\u6211\u4eec\u5c06\u968f\u673a\u62bd\u53d6 1,000 \u6761\u8bc4\u4ef7\u6765\u4ee3\u8868\u6240\u6709\u8bc4\u4ef7\u8005\u7684\u603b\u4f53\u60c5\u611f\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">from random import sample\nsample_reviews = sample(amazon[\"train\"][\"content\"], 1000)<\/pre>\n<p>\u73b0\u5728\uff0c\u6211\u4eec\u6765\u83b7\u53d6\u6bcf\u6761\u8bc4\u4ef7\u7684 VADER \u548c TextBlob \u5206\u6570\u3002 \u6211\u4eec\u5c06\u5faa\u73af\u904d\u5386\u6bcf\u6761\u8bc4\u4ef7\u6587\u672c\uff0c\u901a\u8fc7\u60c5\u611f\u5206\u6790\u5668\u8fd0\u884c\uff0c\u7136\u540e\u5c06\u5206\u6570\u9644\u52a0\u5230\u4e13\u7528\u5217\u8868\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">vader_neg = []\nvader_neu = []\nvader_pos = []\nvader_compound = []\ntextblob_polarity = []\ntextblob_subjectivity = []\n\nfor review in sample_reviews:\n   vader_sent = analyzer.polarity_scores(review)\n   vader_neg += [vader_sent[\"neg\"]]\n   vader_neu += [vader_sent[\"neu\"]]\n   vader_pos += [vader_sent[\"pos\"]]\n   vader_compound += [vader_sent[\"compound\"]]\n  \n   textblob_sent = TextBlob(review).sentiment\n   textblob_polarity += [textblob_sent.polarity]\n   textblob_subjectivity += [textblob_sent.subjectivity]<\/pre>\n<p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u6bcf\u4e2a\u5217\u8868\u4f5c\u4e3a\u5355\u72ec\u7684\u5217\u586b\u5145\u5230 <a href=\"https:\/\/pandas.pydata.org\/\" target=\"_blank\" rel=\"noopener\">pandas<\/a> DataFrame \u4e2d\uff1a<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import pandas as pd\n\nsent_scores = pd.DataFrame({\n   \"vader_neg\": vader_neg,\n   \"vader_neu\": vader_neu,\n   \"vader_pos\": vader_pos,\n   \"vader_compound\": vader_compound,\n   \"textblob_polarity\": textblob_polarity,\n   \"textblob_subjectivity\": textblob_subjectivity\n})<\/pre>\n<p>\u73b0\u5728\uff0c\u53ef\u4ee5\u5f00\u59cb\u63a2\u7d22\u7ed3\u679c\u4e86\u3002<\/p>\n<p>\u901a\u5e38\uff0c\u8fd9\u65f6\u6211\u4eec\u4f1a\u5f00\u59cb\u521b\u5efa\u4e00\u5806\u7528\u4e8e\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\u7684\u4ee3\u7801\u3002 \u53ef\u4ee5\u4f7f\u7528 pandas \u7684 <code>describe<\/code> \u65b9\u6cd5\u83b7\u53d6\u6211\u4eec\u5217\u7684\u6c47\u603b\u7edf\u8ba1\u6570\u636e\uff0c\u5e76\u7f16\u5199 <a href=\"https:\/\/matplotlib.org\/\" target=\"_blank\" rel=\"noopener\">Matplotlib<\/a> \u6216 <a href=\"https:\/\/seaborn.pydata.org\/\" target=\"_blank\" rel=\"noopener\">seaborn<\/a> \u4ee3\u7801\u6765\u76f4\u89c2\u5448\u73b0\u7ed3\u679c\u3002 \u4e0d\u8fc7\uff0cPyCharm \u6709\u4e00\u4e9b\u529f\u80fd\u53ef\u4ee5\u52a0\u5feb\u6574\u4e2a\u8fc7\u7a0b\u3002<\/p>\n<p>\u6211\u4eec\u7ee7\u7eed\uff0c\u6253\u5370 DataFrame\u3002<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">sent_scores<\/pre>\n<p>\u6211\u4eec\u5728\u53f3\u4e0a\u89d2\u53ef\u4ee5\u770b\u5230 <em>Show Column Statistics<\/em>\uff08\u663e\u793a\u5217\u7edf\u8ba1\u4fe1\u606f\uff09\u6309\u94ae\u3002 \u70b9\u51fb\u6309\u94ae\u4f1a\u7ed9\u51fa\u4e24\u4e2a\u9009\u9879\uff1a<em>Compact<\/em>\uff08\u7d27\u51d1\uff09\u548c <em>Detailed<\/em>\uff08\u8be6\u7ec6\uff09\u3002 \u6211\u4eec\u9009\u62e9 <em>Detailed<\/em>\uff08\u8be6\u7ec6\uff09\u3002<\/p>\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532356\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/11\/Screenshot-2024-11-29-at-17.10.32.png\" alt=\"\" width=\"898\" height=\"420\" \/><\/figure>\n<p>\u73b0\u5728\uff0c\u6c47\u603b\u7edf\u8ba1\u4fe1\u606f\u5df2\u7ecf\u4f5c\u4e3a\u5217\u6807\u9898\u7684\u4e00\u90e8\u5206\u63d0\u4f9b\uff01 \u53ef\u4ee5\u770b\u5230 VADER \u590d\u5408\u5206\u6570\u7684\u5e73\u5747\u503c\u662f 0.4\uff08\u4e2d\u4f4d\u6570 = 0.6\uff09\uff0cTextBlob \u6781\u6027\u5206\u6570\u7684\u5e73\u5747\u503c\u662f 0.2\uff08\u4e2d\u4f4d\u6570 = 0.2\uff09\u3002<\/p>\n<p>\u7ed3\u679c\u8868\u660e\uff0c\u5e73\u5747\u800c\u8a00\uff0cVADER \u5bf9\u540c\u4e00\u7ec4\u8bc4\u4ef7\u7684\u8bc4\u4f30\u6bd4 TextBlob \u66f4\u4e3a\u6b63\u9762\u3002 \u5b83\u8fd8\u8868\u660e\uff0c\u5bf9\u4e8e\u8fd9\u4e24\u79cd\u60c5\u611f\u5206\u6790\u5668\u6765\u8bf4\uff0c\u6211\u4eec\u7684\u6b63\u9762\u8bc4\u4ef7\u53ef\u80fd\u591a\u4e8e\u8d1f\u9762\u8bc4\u4ef7 \u2013 \u6211\u4eec\u53ef\u4ee5\u67e5\u770b\u4e00\u4e9b\u53ef\u89c6\u5316\u6548\u679c\u6765\u66f4\u8be6\u7ec6\u5730\u63a2\u8ba8\u8fd9\u4e00\u70b9\u3002<\/p>\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532367\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/11\/Screenshot-2024-11-29-at-17.33.49.png\" alt=\"\" width=\"2816\" height=\"1534\" \/><\/figure>\n<p>\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u7684\u53e6\u4e00\u4e2a PyCharm \u529f\u80fd\u662f DataFrame <em>Chart View<\/em>\uff08\u56fe\u8868\u89c6\u56fe\uff09\u3002 \u8fd9\u4e2a\u529f\u80fd\u7684\u6309\u94ae\u4f4d\u4e8e\u5de6\u4e0a\u89d2\u3002<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532378\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/11\/Screenshot-2024-11-29-at-17.55.46.png\" alt=\"\" width=\"764\" height=\"454\" \/><\/figure>\n<\/div>\n<p>\u70b9\u51fb\u6309\u94ae\u540e\uff0c\u6211\u4eec\u5c06\u5207\u6362\u5230\u56fe\u8868\u7f16\u8f91\u5668\u3002 \u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u4ece DataFrame \u521b\u5efa\u65e0\u4ee3\u7801\u53ef\u89c6\u5316\u3002<\/p>\n<p>\u6211\u4eec\u4ece VADER \u7684\u590d\u5408\u5206\u6570\u5f00\u59cb\u3002 \u8981\u5f00\u59cb\u521b\u5efa\u6b64\u56fe\u8868\uff0c\u8bf7\u8f6c\u5230\u53f3\u4e0a\u89d2\u7684 <em>Show Series Settings<\/em>\uff08\u663e\u793a\u7cfb\u5217\u8bbe\u7f6e\uff09\u3002<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532389\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/11\/Screenshot-2024-11-29-at-17.57.11.png\" alt=\"\" width=\"634\" height=\"368\" \/><\/figure>\n<\/div>\n<p>\u79fb\u9664 <em>X Axis<\/em>\uff08X \u8f74\uff09\u548c <em>Y Axis<\/em>\uff08Y \u8f74\uff09\u7684\u9ed8\u8ba4\u503c\u3002 \u5c06 <em>X Axis<\/em>\uff08X \u8f74\uff09\u503c\u66ff\u6362\u4e3a <code>vader_compound<\/code>\uff0c\u5c06 <em>Y Axis<\/em>\uff08Y \u8f74\uff09\u503c\u66ff\u6362\u4e3a <code>vader_compound<\/code>\u3002 \u70b9\u51fb <em>Y Axis<\/em>\uff08Y \u8f74\uff09\u5b57\u6bb5\u4e2d\u53d8\u91cf\u540d\u79f0\u65c1\u8fb9\u7684\u7bad\u5934\uff0c\u7136\u540e\u9009\u62e9 <code>count<\/code>\u3002<\/p>\n<p>\u6700\u540e\uff0c\u4ece\u56fe\u8868\u56fe\u6807\u4e2d\u9009\u62e9 <em>Histogram<\/em>\uff08\u76f4\u65b9\u56fe\uff09\uff0c\u5c31\u5728 <em>Series Settings<\/em>\uff08\u7cfb\u5217\u8bbe\u7f6e\uff09\u4e0b\u65b9\u3002 VADER \u590d\u5408\u5206\u6570\u53ef\u80fd\u5448\u73b0\u53cc\u5cf0\u5206\u5e03\uff0c\u5728 -0.8 \u5de6\u53f3\u6709\u4e00\u4e2a\u5c0f\u5cf0\u503c\uff0c\u5728 0.9 \u5de6\u53f3\u6709\u4e00\u4e2a\u66f4\u5927\u7684\u5cf0\u503c\u3002 \u8fd9\u4e2a\u5cf0\u503c\u53ef\u80fd\u4ee3\u8868\u4e86\u8d1f\u9762\u8bc4\u4ef7\u548c\u6b63\u9762\u8bc4\u4ef7\u7684\u5206\u88c2\u3002 \u6b63\u9762\u8bc4\u4ef7\u4e5f\u8fdc\u591a\u4e8e\u8d1f\u9762\u8bc4\u4ef7\u3002<\/p>\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532401\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/12\/Screenshot-2024-12-02-at-09.26.23.png\" alt=\"\" width=\"2856\" height=\"1534\" \/><\/figure>\n<p>\u6211\u4eec\u91cd\u590d\u76f8\u540c\u7684\u64cd\u4f5c\uff0c\u521b\u5efa\u4e00\u4e2a\u76f4\u65b9\u56fe\u6765\u67e5\u770b TextBlob \u6781\u6027\u5206\u6570\u7684\u5206\u5e03\u3002<\/p>\n<p>\u76f8\u6bd4\u4e4b\u4e0b\uff0cTextBlob \u503e\u5411\u4e8e\u5c06\u5927\u591a\u6570\u8bc4\u4ef7\u8bc4\u4e3a\u4e2d\u7acb\uff0c\u5f88\u5c11\u6709\u8bc4\u4ef7\u88ab\u8bc4\u4e3a\u5f3a\u70c8\u6b63\u9762\u6216\u8d1f\u9762\u3002 \u4e3a\u4e86\u7406\u89e3\u4e3a\u4ec0\u4e48\u8fd9\u4e24\u4e2a\u60c5\u611f\u5206\u6790\u5668\u63d0\u4f9b\u7684\u5206\u6570\u5b58\u5728\u5dee\u5f02\uff0c\u6211\u4eec\u770b\u4e00\u4e0b VADER \u8bc4\u4e3a\u5f3a\u70c8\u6b63\u9762\u7684\u8bc4\u4ef7\uff0c\u4ee5\u53ca VADER \u8bc4\u4e3a\u5f3a\u70c8\u8d1f\u9762\u4f46 TextBlob \u8bc4\u4e3a\u4e2d\u7acb\u7684\u8bc4\u4ef7\u3002<\/p>\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532412\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/12\/Screenshot-2024-12-02-at-09.32.45.png\" alt=\"\" width=\"2850\" height=\"1528\" \/><\/figure>\n<p>\u6211\u4eec\u53d6\u7b2c\u4e00\u6761 VADER \u8bc4\u4e3a\u6b63\u9762\u4f46 TextBlob \u8bc4\u4e3a\u4e2d\u7acb\u7684\u8bc4\u4ef7\u7684\u7d22\u5f15\uff1a<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">sent_scores[(sent_scores[\"vader_compound\"] &gt;= 0.8) &amp; (sent_scores[\"textblob_polarity\"].between(-0.1, 0.1))].index[0]<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"raw\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">42<\/pre>\n<p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53d6\u7b2c\u4e00\u6761 VADER \u8bc4\u4e3a\u8d1f\u9762\u4f46 TextBlob \u8bc4\u4e3a\u4e2d\u7acb\u7684\u8bc4\u4ef7\u7684\u7d22\u5f15\uff1a<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">sent_scores[(sent_scores[\"vader_compound\"] &lt;= -0.8) &amp; (sent_scores[\"textblob_polarity\"].between(-0.1, 0.1))].index[0]<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"raw\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">0<\/pre>\n<p>\u6211\u4eec\u9996\u5148\u68c0\u7d22\u6b63\u9762\u8bc4\u4ef7\uff1a<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">sample_reviews[42]<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"raw\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">\"I love carpet sweepers for a fast clean up and a way to conserve energy. The Ewbank Multi-Sweep is a solid, well built appliance. However, if you have pets, you will find that it takes more time cleaning the sweeper than it does to actually sweep the room. The Ewbank does pick up pet hair most effectively but emptying it is a bit awkward. You need to take a rag to clean out both dirt trays and then you need a small tooth comb to pull the hair out of the brushes and the wheels. To do a proper cleaning takes quite a bit of time. My old Bissell is easier to clean when it comes to pet hair and it does a great job. If you do not have pets, I would recommend this product because it is definitely well made and for small cleanups, it would suffice. For those who complain about appliances being made of plastic, unfortunately, these days, that's the norm. It's not great and plastic definitely does not hold up but, sadly, product quality is no longer a priority in business.\"<\/pre>\n<p>\u8fd9\u6761\u8bc4\u4ef7\u4f3c\u4e4e\u6b63\u8d1f\u53c2\u534a\uff0c\u4f46\u603b\u4f53\u6bd4\u8f83\u6b63\u9762\u3002<\/p>\n<p>\u73b0\u5728\uff0c\u6211\u4eec\u6765\u770b\u770b\u8d1f\u9762\u8bc4\u4ef7\uff1a<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">sample_reviews[0]<\/pre>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"raw\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">'The only redeeming feature of this Cuisinart 4-cup coffee maker is the sleek black and silver design. After that, it rapidly goes downhill. It is frustratingly difficult to pour water from the carafe into the chamber unless it's done extremely slow and with accurate positioning. Even then, water still tends to dribble out and create a mess. The lid, itself, is VERY poorly designed with it's molded, round \"grip\" to supposedly remove the lid from the carafe. The only way I can remove it is to insert a sharp pointed object into one of the front pouring holes and pry it off! I've also occasionally had a problem with the water not filtering down through the grounds, creating a coffee ground lake in the upper chamber and a mess below. I think the designer should go back to the drawing-board for this one.'<\/pre>\n<p>\u8fd9\u6761\u8bc4\u4ef7\u6beb\u65e0\u7591\u95ee\u662f\u8d1f\u9762\u7684\u3002 \u6bd4\u8f83\u4e24\u8005\uff0cVADER \u4f3c\u4e4e\u66f4\u51c6\u786e\uff0c\u4f46\u5b83\u786e\u5b9e\u503e\u5411\u4e8e\u8fc7\u5ea6\u4f18\u5148\u8003\u8651\u6587\u672c\u4e2d\u7684\u6b63\u9762\u8bcd\u8bed\u3002<\/p>\n<p>\u6211\u4eec\u6700\u540e\u8981\u8003\u8651\u7684\u662f\u6bcf\u6761\u8bc4\u4ef7\u7684\u4e3b\u89c2\u548c\u5ba2\u89c2\u7a0b\u5ea6\u3002 \u4e3a\u6b64\uff0c\u6211\u4eec\u521b\u5efa TextBlob \u4e3b\u89c2\u5206\u6570\u7684\u76f4\u65b9\u56fe\u3002<\/p>\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-532423\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2024\/12\/Screenshot-2024-12-02-at-09.36.19.png\" alt=\"\" width=\"2848\" height=\"1524\" \/><\/figure>\n<p>\u6709\u8da3\u7684\u662f\uff0c\u8bc4\u4ef7\u4e2d\u7684\u4e3b\u89c2\u5206\u5e03\u5f88\u597d\uff0c\u5927\u591a\u6570\u8bc4\u4ef7\u90fd\u662f\u4e3b\u89c2\u548c\u5ba2\u89c2\u4e66\u5199\u7684\u6df7\u5408\u3002 \u5c11\u6570\u8bc4\u4ef7\u4e5f\u975e\u5e38\u4e3b\u89c2\uff08\u63a5\u8fd1 1\uff09\u6216\u975e\u5e38\u5ba2\u89c2\uff08\u63a5\u8fd1 0\uff09\u3002<\/p>\n<p>\u5b83\u4eec\u4e4b\u95f4\u7684\u5206\u6570\u4e3a\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u79cd\u5f88\u597d\u7684\u6570\u636e\u5206\u5272\u65b9\u6cd5\u3002 \u5982\u679c\u9700\u8981\u4e86\u89e3\u4eba\u4eec\u5bf9\u4ea7\u54c1\u7684\u5ba2\u89c2\u8bc4\u4ef7\uff0c\u53ef\u4ee5\u67e5\u770b\u4e3b\u89c2\u5206\u6570\u8f83\u4f4e\u7684\u8bc4\u4ef7\u4ee5\u53ca VADER \u590d\u5408\u5206\u6570\u63a5\u8fd1 1 \u548c -1 \u7684\u8bc4\u4ef7\u3002<\/p>\n<p>\u76f8\u53cd\uff0c\u5982\u679c\u60f3\u4e86\u89e3\u4eba\u4eec\u5bf9\u4ea7\u54c1\u7684\u60c5\u7eea\u53cd\u5e94\uff0c\u60a8\u53ef\u4ee5\u9009\u62e9\u4e3b\u89c2\u5206\u6570\u9ad8\u3001VADER \u590d\u5408\u5206\u6570\u9ad8\u548c\u4f4e\u7684\u8bc4\u4ef7\u3002<\/p>\n<h2 class=\"wp-block-heading\">\u9700\u8981\u8003\u8651\u7684\u4e8b\u60c5<\/h2>\n<p>\u4e0e\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e2d\u7684\u95ee\u9898\u4e00\u6837\uff0c\u8fdb\u884c\u60c5\u611f\u5206\u6790\u65f6\u9700\u8981\u6ce8\u610f\u8bb8\u591a\u4e8b\u9879\u3002<\/p>\n<p>\u6700\u91cd\u8981\u7684\u8003\u91cf\u56e0\u7d20\u4e4b\u4e00\u662f\u5f85\u5206\u6790\u6587\u672c\u7684\u8bed\u8a00\u3002 \u8bb8\u591a\u57fa\u4e8e\u8bcd\u5178\u7684\u65b9\u5f0f\u4ec5\u9002\u7528\u4e8e\u6709\u9650\u6570\u91cf\u7684\u8bed\u8a00\uff0c\u56e0\u6b64\u5982\u679c\u60a8\u4f7f\u7528\u8fd9\u4e9b\u8bcd\u5178\u4e0d\u652f\u6301\u7684\u8bed\u8a00\uff0c\u5219\u53ef\u80fd\u9700\u8981\u91c7\u7528\u53e6\u4e00\u79cd\u65b9\u5f0f\uff0c\u4f8b\u5982\u4f7f\u7528\u5fae\u8c03\u7684 LLM \u6216\u8bad\u7ec3\u60a8\u81ea\u5df1\u7684\u6a21\u578b\u3002<\/p>\n<p>\u968f\u7740\u6587\u672c\u590d\u6742\u5ea6\u7684\u589e\u52a0\uff0c\u57fa\u4e8e\u8bcd\u5178\u7684\u5206\u6790\u5668\u548c\u57fa\u4e8e\u8bcd\u888b\u7684\u6a21\u578b\u4e5f\u4f1a\u5f88\u96be\u6b63\u786e\u68c0\u6d4b\u60c5\u611f\u3002 \u8bbd\u523a\u6216\u66f4\u5fae\u5999\u7684\u4e0a\u4e0b\u6587\u6307\u6807\u5bf9\u4e8e\u7b80\u5355\u7684\u6a21\u578b\u6765\u8bf4\u53ef\u80fd\u5f88\u96be\u68c0\u6d4b\u5230\uff0c\u5e76\u4e14\u8fd9\u4e9b\u6a21\u578b\u53ef\u80fd\u65e0\u6cd5\u51c6\u786e\u5730\u5bf9\u6b64\u7c7b\u6587\u672c\u7684\u60c5\u611f\u8fdb\u884c\u5206\u7c7b\u3002 LLM \u53ef\u80fd\u80fd\u591f\u5904\u7406\u66f4\u590d\u6742\u7684\u6587\u672c\uff0c\u4f46\u60a8\u9700\u8981\u5c1d\u8bd5\u4e0d\u540c\u7684\u6a21\u578b\u3002<\/p>\n<p>\u6700\u540e\uff0c\u5728\u8fdb\u884c\u60c5\u611f\u5206\u6790\u65f6\uff0c\u4e5f\u4f1a\u51fa\u73b0\u4e0e\u5904\u7406\u4efb\u4f55\u673a\u5668\u5b66\u4e60\u95ee\u9898\u65f6\u76f8\u540c\u7684\u95ee\u9898\u3002 \u60a8\u7684\u6a21\u578b\u7684\u597d\u574f\u53d6\u51b3\u4e8e\u4f7f\u7528\u7684\u8bad\u7ec3\u6570\u636e\u3002 \u5982\u679c\u65e0\u6cd5\u83b7\u5f97\u9002\u5408\u95ee\u9898\u9886\u57df\u7684\u9ad8\u8d28\u91cf\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6570\u636e\u96c6\uff0c\u60a8\u5c06\u65e0\u6cd5\u6b63\u786e\u9884\u6d4b\u76ee\u6807\u53d7\u4f17\u7684\u60c5\u611f\u3002<\/p>\n<p>\u60a8\u8fd8\u5e94\u8be5\u786e\u4fdd\u76ee\u6807\u9002\u5408\u4e1a\u52a1\u95ee\u9898\u3002 \u5efa\u7acb\u6a21\u578b\u6765\u4e86\u89e3\u4ea7\u54c1\u662f\u5426\u4f1a\u8ba9\u5ba2\u6237\u611f\u5230\u201c\u4f24\u5fc3\u201d\u3001\u201c\u6124\u6012\u201d\u6216\u201c\u538c\u6076\u201d\u53ef\u80fd\u770b\u8d77\u6765\u5f88\u6709\u5438\u5f15\u529b\uff0c\u4f46\u5982\u679c\u8fd9\u4e0d\u80fd\u5e2e\u52a9\u60a8\u51b3\u5b9a\u5982\u4f55\u6539\u8fdb\u4ea7\u54c1\uff0c\u90a3\u4e48\u5b83\u5c31\u65e0\u6cd5\u89e3\u51b3\u60a8\u7684\u95ee\u9898\u3002<\/p>\n<h2 class=\"wp-block-heading\">\u603b\u7ed3<\/h2>\n<p>\u5728\u8fd9\u7bc7\u535a\u6587\u4e2d\uff0c\u6211\u4eec\u6df1\u5165\u63a2\u8ba8\u4e86 Python \u60c5\u611f\u5206\u6790\u8fd9\u4e2a\u6709\u8da3\u7684\u9886\u57df\uff0c\u5e76\u5c55\u793a\u4e86\u5982\u4f55\u901a\u8fc7\u4e00\u7cfb\u5217\u5f3a\u5927\u7684\u8f6f\u4ef6\u5305\u8ba9\u8fd9\u4e2a\u590d\u6742\u7684\u9886\u57df\u66f4\u6613\u7406\u89e3\u3002<\/p>\n<p>\u6211\u4eec\u4ecb\u7ecd\u4e86\u60c5\u611f\u5206\u6790\u7684\u6f5c\u5728\u5e94\u7528\u3001\u8bc4\u4f30\u60c5\u611f\u7684\u4e0d\u540c\u65b9\u5f0f\u4ee5\u53ca\u4ece\u6587\u672c\u4e2d\u63d0\u53d6\u60c5\u611f\u7684\u4e3b\u8981\u65b9\u5f0f\u3002 \u6211\u4eec\u8fd8\u770b\u5230 PyCharm \u4e2d\u7684\u4e00\u4e9b\u5b9e\u7528\u529f\u80fd\uff0c\u5b83\u4eec\u8ba9\u4f7f\u7528\u6a21\u578b\u548c\u89e3\u91ca\u5176\u7ed3\u679c\u53d8\u5f97\u66f4\u7b80\u5355\u3001\u66f4\u5feb\u6377\u3002<\/p>\n<p>\u867d\u7136\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u76ee\u524d\u4e3b\u8981\u5173\u6ce8\u5927\u8bed\u8a00\u6a21\u578b\uff0c\u4f46\u4f7f\u7528\u57fa\u4e8e\u8bcd\u5178\u7684\u5206\u6790\u5668\u6216\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff08\u5982\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668\uff09\u7684\u65e7\u6280\u672f\u5728\u60c5\u611f\u5206\u6790\u4e2d\u4ecd\u7136\u5360\u6709\u4e00\u5e2d\u4e4b\u5730\u3002 \u5728\u5206\u6790\u8f83\u7b80\u5355\u7684\u6587\u672c\u65f6\uff0c\u6216\u8005\u5f53\u901f\u5ea6\u3001\u9884\u6d4b\u6216\u6613\u4e8e\u90e8\u7f72\u662f\u4f18\u5148\u4e8b\u9879\u65f6\uff0c\u8fd9\u4e9b\u6280\u672f\u975e\u5e38\u6709\u7528\u3002 LLM \u6700\u9002\u5408\u66f4\u590d\u6742\u6216\u66f4\u7ec6\u5fae\u7684\u6587\u672c\u3002<\/p>\n<p>\u73b0\u5728\uff0c\u60a8\u5df2\u7ecf\u638c\u63e1\u4e86\u57fa\u7840\u77e5\u8bc6\uff0c\u53ef\u4ee5\u5728\u6211\u4eec\u7684\u6559\u7a0b\u4e2d\u5b66\u4e60\u5982\u4f55<a href=\"https:\/\/blog.jetbrains.com\/pycharm\/2024\/12\/how-to-do-sentiment-analysis-with-large-language-models\/\" data-type=\"link\" data-id=\"https:\/\/blog.jetbrains.com\/pycharm\/2024\/12\/how-to-do-sentiment-analysis-with-large-language-models\/\">\u4f7f\u7528 LLM \u8fdb\u884c\u60c5\u611f\u5206\u6790<\/a>\u3002 \u5206\u6b65\u6307\u5357\u53ef\u4ee5\u5e2e\u52a9\u60a8\u4e86\u89e3\u5982\u4f55\u4e3a\u4efb\u52a1\u9009\u62e9\u6b63\u786e\u7684\u6a21\u578b\u3001\u4f7f\u7528\u5b83\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff0c\u751a\u81f3\u81ea\u884c\u5fae\u8c03\u3002<\/p>\n<p>\u9605\u8bfb\u8fd9\u7bc7\u535a\u6587\u540e\uff0c\u5982\u679c\u60a8\u60f3\u8fdb\u4e00\u6b65\u66f4\u5e7f\u6cdb\u5730\u5b66\u4e60\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6216\u673a\u5668\u5b66\u4e60\uff0c\u53ef\u4ee5\u53c2\u8003\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ul>\n<li><a href=\"https:\/\/blog.jetbrains.com\/pycharm\/2024\/12\/how-to-do-sentiment-analysis-with-large-language-models\/\" data-type=\"link\" data-id=\"https:\/\/blog.jetbrains.com\/pycharm\/2024\/12\/how-to-do-sentiment-analysis-with-large-language-models\/\">Learn how to do sentiment analysis with large language models<\/a><\/li>\n<li><a href=\"https:\/\/blog.jetbrains.com\/pycharm\/2022\/06\/start-studying-machine-learning-with-pycharm\/\">Start studying machine learning with PyCharm<\/a><\/li>\n<li><a href=\"https:\/\/lp.jetbrains.com\/research\/ml_methods\/\" target=\"_blank\" rel=\"noopener\" data-type=\"link\" data-id=\"https:\/\/lp.jetbrains.com\/research\/ml_methods\/\">Explore machine learning methods in software engineering<\/a><\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\">\u7acb\u5373\u5f00\u59cb\u4f7f\u7528 PyCharm \u8fdb\u884c\u60c5\u611f\u5206\u6790<\/h2>\n<p>\u5982\u679c\u60a8\u73b0\u5728\u6253\u7b97\u5f00\u59cb\u81ea\u5df1\u7684\u60c5\u611f\u5206\u6790\u9879\u76ee\uff0c\u60a8\u53ef\u4ee5\u6fc0\u6d3b PyCharm \u7684\u4e09\u4e2a\u6708\u514d\u8d39\u8ba2\u9605\u3002 \u70b9\u51fb\u4e0b\u65b9\u94fe\u63a5\uff0c\u8f93\u5165\u6b64\u4fc3\u9500\u4ee3\u7801\uff1a<strong>PCSA24<\/strong>\u3002 \u7136\u540e\uff0c\u60a8\u5c06\u901a\u8fc7\u7535\u5b50\u90ae\u4ef6\u6536\u5230\u6fc0\u6d3b\u7801\u3002<\/p>\n<div class=\"buttons\">\n<div class=\"buttons__row\"><a class=\"btn\" href=\"https:\/\/www.jetbrains.com.cn\/store\/redeem\/\" target=\"\" rel=\"noopener\">\u6fc0\u6d3b 3 \u4e2a\u6708\u8ba2\u9605<\/a><\/div>\n<\/div>\n\n\n<p><\/p>\n\n\n\n<p>\u672c\u535a\u6587\u82f1\u6587\u539f\u4f5c\u8005\uff1a<\/p>\n\n\n    <div class=\"about-author \">\n        <div class=\"about-author__box\">\n            <div class=\"row\">\n                <div class=\"about-author__box-img\">\n                    <img decoding=\"async\" src=\"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2022\/11\/BK7A9876_korr_sRGB_8_1000x1500px_square_resized-200x200.jpg\" width=\"200\" height=\"200\" alt=\"Jodie Burchell\" loading=\"lazy\"  class=\"avatar avatar-200 wp-user-avatar wp-user-avatar-200 photo avatar-default\">\n                <\/div>\n                <div class=\"about-author__box-text\">\n                                            <h4>Jodie Burchell<\/h4>\n                                        <p>Dr. Jodie Burchell is the Developer Advocate in Data Science at JetBrains, and was previously a Lead Data Scientist at Verve Group Europe. She completed a PhD in clinical psychology and a postdoc in biostatistics, before leaving academia for a data science career. She has worked for 7 years as a data scientist in both Australia and Germany, developing a range of products including recommendation systems, analysis platforms, search engine improvements and audience profiling. She has held a broad range of responsibilities in her career, doing everything from data analytics to maintaining machine learning solutions in production. She is a long time content creator in data science, across conference and user group presentations, books, webinars, and posts on both her own and JetBrain&#8217;s blogs.<\/p>\n                <\/div>\n            <\/div>\n        <\/div>\n    <\/div>\n","protected":false},"author":1297,"featured_media":549212,"comment_status":"closed","ping_status":"closed","template":"","categories":[952],"tags":[8557,3252,5377,8652],"cross-post-tag":[],"acf":[],"_links":{"self":[{"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/pycharm\/549160"}],"collection":[{"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/pycharm"}],"about":[{"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/types\/pycharm"}],"author":[{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/users\/1297"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/comments?post=549160"}],"version-history":[{"count":8,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/pycharm\/549160\/revisions"}],"predecessor-version":[{"id":550139,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/pycharm\/549160\/revisions\/550139"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/media\/549212"}],"wp:attachment":[{"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/media?parent=549160"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/categories?post=549160"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/tags?post=549160"},{"taxonomy":"cross-post-tag","embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/cross-post-tag?post=549160"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}