python 提取文本关键词(python提取文本关键字)

内容来源：他家真能上更新时间：2023-05-29 23:02:53

你是不是想了解python 提取文本关键词的相关内容，下方文本框内是根据关键词python 提取文本关键词生成的详细文字解说内容。在文本框下方还有跟python 提取文本关键词相关的素材（来自互联网，禁止商用）、相关话题（来自访客喜好数据）、相关热点（来自24小时内全网热点智能聚合），希望这些内容都可以帮到你。

您的运行环境禁止了JavaScript的执行，请开启后重新打开该页面！如何用Python提取中文关键词？-简书var_hmt=_hmt||[];(function(){varhm=document.createElement("script");hm.src="//hm.baidu.com/hm.js?0c0e9d9b1e7d617b3e6842e85b9fb068";vars=document.getElementsByTagName("script")[0];s.parentNode.insertBefore(hm,s)})();(function(){varbp=document.createElement('script');varcurProtocol=window.location.protocol.split(':')[0];if(curProtocol==='https'){bp.src='https://zz.bdstatic.com/linksubmit/push.js'}else{bp.src='http://push.zhanzhang.baidu.com/push.js'}vars=document.getElementsByTagName("script")[0];s.parentNode.insertBefore(bp,s)})();document.write(unescape("%3Cspanstyle='display:none'id='cnzz_stat_icon_1279807957'%3E%3C/span%3E%3Cscriptsrc='https://v1.cnzz.com/z_stat.php%3Fid%3D1279807957'type='text/javascript'%3E%3C/script%3E"));{"title":"如何用Python提取中文关键词？","description":"本文一步步为你演示，如何用Python从中文文本中提取关键词。如果你需要对长文“观其大略”，不妨尝试一下。需求好友最近对自然语言处理感兴趣，因为他打算利用自动化方法从长文...","imageUrl":"http://upload-images.jianshu.io/upload_images/64542-2bc0d58a72fe345d.png"}#layout-default{min-height:100vh;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-box-orient:vertical;-webkit-box-direction:normal;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column}#layout-default>.main-view{-webkit-box-flex:1;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;padding-left:18px;padding-right:18px}.header-wrap#jianshu-header[data-v-b8360f46]{z-index:10;position:fixed;left:0;right:0;padding:3px18px;font-size:0;background-color:#fff}body.reader-night-mode.header-wrap#jianshu-header[data-v-b8360f46]{background-color:#3f3f3f}.header-wrap#jianshu-header.header-normal[data-v-b8360f46]{top:0;-webkit-box-shadow:02px4pxrgba(0,0,0,.1);box-shadow:02px4pxrgba(0,0,0,.1);height:48px}.header-wrap#jianshu-header.header-wechat[data-v-b8360f46]{bottom:0;-webkit-box-shadow:0-2px4pxrgba(0,0,0,.1);box-shadow:0-2px4pxrgba(0,0,0,.1);padding-bottom:-webkit-calc(3px+env(safe-area-inset-bottom));padding-bottom:calc(3px+env(safe-area-inset-bottom));padding-bottom:-webkit-calc(3px+constant(safe-area-inset-bottom));padding-bottom:calc(3px+constant(safe-area-inset-bottom))}.header-wrap#jianshu-header.from-baidu[data-v-b8360f46]{position:static;margin-bottom:4px}.header-wrap#jianshu-header.logo-wrap[data-v-b8360f46]{height:100%;float:left}.header-wrap#jianshu-header.logo[data-v-b8360f46]{height:100%;vertical-align:middle}.header-wrap#jianshu-header.slogan[data-v-b8360f46]{line-height:20px;vertical-align:middle;margin-left:12px;display:inline-block;font-size:16px;color:#333}body.reader-night-mode.header-wrap#jianshu-header.slogan[data-v-b8360f46]{color:#c8c8c8}.header-wrap#jianshu-header.slogan.slogan-title[data-v-b8360f46]{font-size:16px;line-height:20px;margin-top:2px;max-width:120px;color:#333}body.reader-night-mode.header-wrap#jianshu-header.slogan.slogan-title[data-v-b8360f46]{color:#c8c8c8}.header-wrap#jianshu-header.slogan.slogan-sub-title[data-v-b8360f46]{font-size:12px;color:#999}.header-wrap#jianshu-header.header-download[data-v-b8360f46]{position:absolute;padding:8px15px;right:0;top:0;font-size:16px;color:#ea6f5a}.header-wrap#jianshu-header.header-downloadbutton[data-v-b8360f46]{color:#fff;background-color:#ea6f5a;width:92px;height:32px;border-radius:16px;font-size:15px}.header-wrap#jianshu-header.collection-avatar[data-v-b8360f46]{width:54px;height:54px;border-radius:10%;margin-top:-18px;vertical-align:middle}.header-wrap#jianshu-header.user-avatar[data-v-b8360f46]{width:54px;height:54px;border-radius:50%;margin-top:-18px;vertical-align:middle}.header-wrap.header-shim[data-v-b8360f46]{height:52px}#note-show.article-info.info:after,#note-show.article-info.info:before{content:"";clear:both;display:table}#note-show.book.content,#note-show.book.contentblockquote,#note-show.book.contentol,#note-show.book.contentp,#note-show.book.contentpre,#note-show.book.contentul,#note-show.book.title,#note-show.note.content,#note-show.note.contentblockquote,#note-show.note.contentol,#note-show.note.contentp,#note-show.note.contentpre,#note-show.note.contentul,#note-show.note.title{word-break:break-word!important;word-break:break-all}.fubiao-dialog{position:fixed;top:auto!important;bottom:100px!important;right:30px!important;width:66px;height:66px;z-index:999999;-webkit-background-size:100%100%;background-size:100%}@-webkit-keyframesscale2{0%{-webkit-transform:scale(.6);transform:scale(.6)}50%{-webkit-transform:scale(1);transform:scale(1)}to{-webkit-transform:scale(.6);transform:scale(.6)}}@keyframesscale2{0%{-webkit-transform:scale(.6);transform:scale(.6)}50%{-webkit-transform:scale(1);transform:scale(1)}to{-webkit-transform:scale(.6);transform:scale(.6)}}#note-locked{min-height:90vh;padding-left:0!important;padding-right:0!important}#note-locked.logo{position:absolute;height:48px;left:10px;top:10px}#note-locked.error-main{position:absolute;text-align:center;top:50%;left:50%;width:80%;-webkit-transform:translate(-50%,-50%);-ms-transform:translate(-50%,-50%);transform:translate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文我会给出相关的资料链接。如果你对细节感兴趣，欢迎按图索骥，查阅学习。先说TF-idf。它的全称是TermFrequency-inversedocumentfrequency。中间有个连字符，左右两侧各是一部分，共同结合起来，决定某个词的重要程度。第一部分，就是词频（TermFrequency），即某个词语出现的频率。我们常说“重要的事说三遍”。同样的道理，某个词语出现的次数多，也就说明这个词语重要性可能会很高。但是，这只是可能性，并不绝对。例如现代汉语中的许多虚词——“的，地，得”，古汉语中的许多句尾词“之、乎、者、也、兮”，这些词在文中可能出现许多次，但是它们显然不是关键词。这就是为什么我们在判断关键词的时候，需要第二部分（idf）配合。逆文档频率（inversedocumentfrequency）首先计算某个词在各文档中出现的频率。假设一共有10篇文档，其中某个词A在其中10篇文章中都出先过，另一个词B只在其中3篇文中出现。请问哪一个词更关键？给你一分钟思考一下，然后继续读。公布答案时间到。答案是B更关键。A可能就是虚词，或者全部文档共享的主题词。而B只在3篇文档中出现，因此很有可能是个关键词。逆文档频率就是把这种文档频率取倒数。这样第一部分和第二部分都是越高越好。二者都高，就很有可能是关键词了。TF-idf讲完了，下面我们说说TextRank。相对于TF-idf，TextRank要显得更加复杂一些。它不是简单做加减乘除运算，而是基于图的计算。下图是原始文献中的示例图。image.pngTextRank首先会提取词汇，形成节点；然后依据词汇的关联，建立链接。依照连接节点的多少，给每个节点赋予一个初始的权重数值。然后就开始迭代。根据某个词所连接所有词汇的权重，重新计算该词汇的权重，然后把重新计算的权重传递下去。直到这种变化达到均衡态，权重数值不再发生改变。这与Google的网页排名算法PageRank，在思想上是一致的。image.png根据最后的权重值，取其中排列靠前的词汇，作为关键词提取结果。如果你对原始文献感兴趣，请参考以下链接：TF-idf原始文献链接。TextRank原始文献链接。讨论小结一下，本文探讨了如何用Python对中文文本做关键词提取。具体而言，我们分别使用了TF-idf和TextRank方法，二者提取关键词的结果可能会有区别。你做过中文关键词提取吗？使用的是什么工具？它的效果如何？有没有比本文更高效的方法？欢迎留言，把你的经验和思考分享给大家，我们一起交流讨论。喜欢请点赞。还可以微信关注和置顶我的公众号“玉树芝兰”(nkwangshuyi)。如果你对数据科学感兴趣，不妨阅读我的系列教程索引贴《如何高效入门数据科学？》，里面还有更多的有趣问题及解法。©著作权归作者所有,转载或内容合作请联系作者上一章如何用Markdown写论文？下一章如何用Python读取开放数据？创作你的创作，接受世界的赞赏登录|打开App|热门文章下载简书，随时随地看好文window.__INITIAL_STATE__={"fromBaidu":false,"csrfToken":"VLYjGBfDkRbuykwj1MtEx4oVs6qymkoFgUXoGc0r0B1w2HXATz0Q6SRLNyj2dqLec2CmqdOA8kloJ6QqE7Kbag==","user":null,"login":false,"isAuth":true,"haveAuthToken":false,"ssrContext":{"header":{"host":"www.jianshu.com","x-forwarded-host":"www.jianshu.com","x-request-start":"t=1685373173.259","x-forwarded-server":"www.jianshu.com","x-real-ip":"39.96.151.153","x-forwarded-for":"43.159.41.36,39.96.151.153,39.96.151.153,39.96.151.153,10.100.0.196","x-forwarded-proto":"http","remoteip":"39.96.151.153","user-agent":"Mozilla\u002F5.0(Linux;Android12;PGZ110Build\u002FSP1A.210812.016)AppleWebKit\u002F537.36(KHTML,likeGecko)Chrome\u002F97.0.4692.98MobileSafari\u002F537.36","x-true-ip":"39.96.151.153","x-client-ip":"43.159.41.36","web-server-type":"nginx","wl-proxy-client-ip":"39.96.151.153","eagleeye-traceid":"a3b579a916853731731827880e","via":"vn26.l1,l2vn2.l2,l2cm12-6.l2","ali-cdn-real-port":"55962","ali-cdn-real-ip":"43.159.41.36","ali-swift-log-host":"www.jianshu.com","ali-swift-stat-host":"www.jianshu.com","x-client-scheme":"https","ali-swift-force-ttl-code":"400=0","referer":"https:\u002F\u002Fwww.jianshu.com\u002Fp\u002Fcf383fd471bb","accept":"text\u002Fhtml,application\u002Fxhtml+xml,application\u002Fxml;q=0.9,image\u002Fwebp,image\u002Fapng,*\u002F*;q=0.8,application\u002Fsigned-exchange;v=b3","accept-encoding":"gzip,deflate,br","accept-language":"zh-CN,zh;q=0.9","dnt":"1","origin":"http:\u002F\u002Fwww.jianshu.com","sec-fetch-dest":"document","sec-fetch-mode":"navigate","sec-fetch-site":"none","sec-fetch-user":"?1","ali-cdn-appview-name":"cdn-tengine","ali-swift-origin-scheme":"https","ali-swift-origin-port":"443","ali-cdn-adaptive-ports":"80,443","x-alicdn-da-via":"163.181.121.242,156.59.137.249,111.13.222.135","eagleeye-rpcid":"0.1","wf":"1","x5-uuid":"12096d3d2fbccb135fb75a963d6c647f","x-envoy-external-address":"10.100.0.196","x-request-id":"63524e9f-86ad-4ae2-aa6d-e582e4d7ce5c","content-length":"0","request-id":"69653c01-dcc2-4861-b7c9-d0e22a7e9757"},"url":"\u002Fp\u002Fcf383fd471bb","method":"GET"},"ua":{"ios":false,"android":true,"wechat":false,"baiduspider":false,"qq":false,"qqBrowser":false,"tencent":false,"alipay":false,"weibo":false,"pc":false,"others":false,"app":false,"baiduapp":false,"toutiaoapp":false},"wechat":{},"mode":"reader-day-mode","lang":"zh-CN","font":"normal-size","wechatAppId":"","wechatOpenId":"","requestId":"69653c01-dcc2-4861-b7c9-d0e22a7e9757","viaId":"none","downloads":{"hugo":{"yingyongbao":"https:\u002F\u002Fitunes.apple.com\u002Fapp\u002Fapple-store\u002Fid888237539?pt=69349191&ct=weixin_h5_1&mt=8","weibo":"https:\u002F\u002Fm.weibo.cn\u002Fp\u002F1004041843659","official":"https:\u002F\u002Fitunes.apple.com\u002Fcn\u002Fapp\u002Fjian-shu\u002Fid888237539?ls=1&mt=8"},"haruki":{"yingyongbao":"https:\u002F\u002Fa.app.qq.com\u002Fo\u002Fsimple.jsp?pkgname=com.jianshu.haruki&ckey=CK1387916129227","weibo":"https:\u002F\u002Fm.weibo.cn\u002Fp\u002F1004041843659","official":"https:\u002F\u002Fdownloads.jianshu.io\u002Fapps\u002Fharuki\u002FJianshu-official-6.5.3-2022006053-1670050861.apk"}},"abTest":1,"auditing":0,"grayRelease":1,"club":{"prices":[],"days":"","jsb":{},"transactions":{"has_more":false,"transactions":[]},"inAppleTrial":null,"member_iap_without_login_enabled":null},"collection":{"info":null,"recommended":{"data":[],"takeCount":0,"totalPage":0,"curPage":0,"noMore":false},"categories":[]},"app":{"audios":{"book":null,"list":[]},"lhsd":{},"withdraw":null,"userAvatar":null,"inviteUser":null,"notifications":{"others":{"data":[],"totalPage":0}},"pts":null,"ptsScore":null,"hotIssues":null,"books":{"data":[],"total":0,"name":""},"knowledgeShop":{"publications":{"data":[],"state":{"page":1,"count":10,"noMore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和数据。请从\u003Cahref=\"https:\u002F\u002Fgithub.com\u002Fwshuyi\u002Fdemo-keyword-extraction\u002Farchive\u002Fmaster.zip\"target=\"_blank\"rel=\"nofollow\"\u003E这个地址\u003C\u002Fa\u003E下载压缩包文件，然后解压。\u003C\u002Fp\u003E\n\u003Cp\u003E解压后的目录名称为\u003Ccode\u003Edemo-keyword-extraction-master\u003C\u002Fcode\u003E，样例目录包含以下内容：\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:700px;max-height:439px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:62.81%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"1764\"data-height=\"1108\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-93789a87fdf0e99d.jpg\"data-original-width=\"1764\"data-original-height=\"1108\"data-original-format=\"\"data-original-filesize=\"346097\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003E2017-12-7_21-24-56_snapshots-01.jpg\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003E除了README.md这个github项目默认说明文件外，目录下还有两个文件，分别是数据文件sample.txt和程序源代码文件demo-extract-keyword.ipynb。\u003C\u002Fp\u003E\n\u003Ch2\u003E结巴分词\u003C\u002Fh2\u003E\n\u003Cp\u003E我们使用的关键词提取工具为\u003Cahref=\"https:\u002F\u002Fgithub.com\u002Ffxsjy\u002Fjieba\"target=\"_blank\"rel=\"nofollow\"\u003E结巴分词\u003C\u002Fa\u003E。\u003C\u002Fp\u003E\n\u003Cp\u003E之前在《\u003Cahref=\"https:\u002F\u002Fwww.jianshu.com\u002Fp\u002F721190534061\"target=\"_blank\"\u003E如何用Python做中文分词？\u003C\u002Fa\u003E》一文中，我们曾经使用过该工具为中文语句做分词。这次我们使用的，是它的另一项功能，即关键词提取。\u003C\u002Fp\u003E\n\u003Cp\u003E请进入终端，使用cd命令进入解压后的文件夹demo-keyword-extraction-master，输入以下命令：\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccode\u003Epipinstalljieba\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E好了，软件包工具也已经准备就绪。下面我们执行\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccode\u003Ejupyternotebook\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E进入到Jupyter笔记本环境。\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:700px;max-height:437px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:62.5%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"2560\"data-height=\"1600\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-e37d88cbd92744f5.png\"data-original-width=\"2560\"data-original-height=\"1600\"data-original-format=\"\"data-original-filesize=\"202787\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003Eimage.png\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003E到这里，环境已经准备好了，我们下面来介绍本文使用的中文文本数据。\u003C\u002Fp\u003E\n\u003Ch1\u003E数据\u003C\u002Fh1\u003E\n\u003Cp\u003E一开始，我还曾为寻找现成的中文文本发愁。\u003C\u002Fp\u003E\n\u003Cp\u003E网上可以找到的中文文本浩如烟海。\u003C\u002Fp\u003E\n\u003Cp\u003E但是拿来做演示，是否会有版权问题，我就不确定了。万一把哪位大家之作拿来做了分析，人家可能就要过问一句“这电子版你是从哪里搞到的啊？”\u003C\u002Fp\u003E\n\u003Cp\u003E万一再因此提出诉讼，我可无法招架。\u003C\u002Fp\u003E\n\u003Cp\u003E后来发现，我这简直就是自寻烦恼——找别人的文本干什么？用我自己的不就好了？\u003C\u002Fp\u003E\n\u003Cp\u003E这一年多以来，我写的文章已有90多篇，总字数已经超过了27万。\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:700px;max-height:342px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:40.52%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"844\"data-height=\"342\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-cff65832d949ab75.png\"data-original-width=\"844\"data-original-height=\"342\"data-original-format=\"\"data-original-filesize=\"130086\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003Eimage.png\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003E我特意从中找了一篇非技术性的，以避免提取出的关键词全都是Python命令。\u003C\u002Fp\u003E\n\u003Cp\u003E我选取的，是去年的那篇《\u003Cahref=\"https:\u002F\u002Fwww.jianshu.com\u002Fp\u002F6941a40df96c\"target=\"_blank\"\u003E网约车司机二三事\u003C\u002Fa\u003E》。\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:700px;max-height:437px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:62.5%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"2560\"data-height=\"1600\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-58168bc3522b9fd7.png\"data-original-width=\"2560\"data-original-height=\"1600\"data-original-format=\"\"data-original-filesize=\"1094360\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003Eimage.png\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003E\u003Cahref=\"https:\u002F\u002Fwww.jianshu.com\u002Fp\u002F6941a40df96c\"target=\"_blank\"\u003E这篇文章\u003C\u002Fa\u003E，讲的都是些比较有趣的小故事。\u003C\u002Fp\u003E\n\u003Cp\u003E我从网页上摘取文字，存储到sample.txt中。\u003C\u002Fp\u003E\n\u003Cp\u003E注意，这里是很容易踩坑的地方。在夏天的一次工作坊教学中，好几位同学因为从网上摘取中文文本出现问题，卡住很长时间。\u003C\u002Fp\u003E\n\u003Cp\u003E这是因为不同于英语，汉字有\u003Cstrong\u003E编码\u003C\u002Fstrong\u003E问题。不同系统都有不同的默认编码，不同版本的Python接受的编码也不同。你从网上下载的文本文件，也可能与你系统的编码不统一。\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:700px;max-height:562px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:36.4%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"1544\"data-height=\"562\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-7510c535903f92d0.png\"data-original-width=\"1544\"data-original-height=\"562\"data-original-format=\"\"data-original-filesize=\"219718\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003Eimage.png\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003E不论如何，这些因素都有可能导致你打开后的文本里，到处都是看不懂的乱码。\u003C\u002Fp\u003E\n\u003Cp\u003E因而，正确的使用中文文本数据方式，是你在JupyterNotebook里面，新建一个文本文件。\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:416px;max-height:520px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:125.0%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"416\"data-height=\"520\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-6e0d4177f370cf1c.png\"data-original-width=\"416\"data-original-height=\"520\"data-original-format=\"\"data-original-filesize=\"65334\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003Eimage.png\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003E然后，会出现以下的空白文件。\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:700px;max-height:437px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:62.5%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"2560\"data-height=\"1600\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-b7d4a092f7c4b301.png\"data-original-width=\"2560\"data-original-height=\"1600\"data-original-format=\"\"data-original-filesize=\"86853\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003Eimage.png\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003E把你从别处下载的文本，用任意一种\u003Cstrong\u003E能正常显示\u003C\u002Fstrong\u003E的编辑器打开，然后拷贝全部内容，粘贴到这个空白文本文件中，就能避免编码错乱。\u003C\u002Fp\u003E\n\u003Cp\u003E避开了这个坑，可以为你节省很多不必要的烦恼尝试。\u003C\u002Fp\u003E\n\u003Cp\u003E好了，知道了这个窍门，下面你就能愉快地进行关键词提取了。\u003C\u002Fp\u003E\n\u003Ch1\u003E执行\u003C\u002Fh1\u003E\n\u003Cp\u003E回到JupyterNotebook的主界面，点击demo-extract-keyword.ipynb，你就能看到源码了。\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:700px;max-height:437px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:62.5%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"2560\"data-height=\"1600\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-f7dde364bccf9112.png\"data-original-width=\"2560\"data-original-height=\"1600\"data-original-format=\"\"data-original-filesize=\"625453\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003Eimage.png\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003E对，你没看错。只需要这短短的4个语句，就能完成两种不同方式（TF-idf与TextRank）的关键词提取。\u003C\u002Fp\u003E\n\u003Cp\u003E本部分我们先讲解执行步骤。不同关键词提取方法的原理，我们放在后面介绍。\u003C\u002Fp\u003E\n\u003Cp\u003E首先我们从结巴分词的分析工具箱里导入所有的关键词提取功能。\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccodeclass=\"python\"\u003Efromjieba.analyseimport*\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E在对应的语句上，按下Shift+Enter组合按键，就可以执行语句，获取结果了。\u003C\u002Fp\u003E\n\u003Cp\u003E然后，让Python打开我们的样例文本文件，并且读入其中的全部内容到data变量。\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccodeclass=\"python\"\u003Ewithopen('sample.txt')asf:\ndata=f.read()\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E使用TF-idf方式提取关键词和权重，并且依次显示出来。如果你不做特殊指定的话，默认显示数量为20个关键词。\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccodeclass=\"python\"\u003Eforkeyword,weightinextract_tags(data,withWeight=True):\nprint('%s%s'%(keyword,weight))\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E显示内容之前，会有一些提示，不要管它。\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccode\u003EBuildingprefixdictfromthedefaultdictionary...\nLoadingmodelfromcache\u002Fvar\u002Ffolders\u002F8s\u002Fk8yr4zy52q1dh107gjx280mw0000gn\u002FT\u002Fjieba.cache\nLoadingmodelcost0.547seconds.\nPrefixdicthasbeenbuiltsuccesfully.\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E然后列表就出来了：\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccode\u003E优步0.280875594782\n司机0.119951947597\n乘客0.105486129485\n师傅0.0958888107815\n张师傅0.0838162334963\n目的地0.0753618512886\n网约车0.0702188986954\n姐姐0.0683412127766\n自己0.0672533110661\n上车0.0623276916308\n活儿0.0600134354214\n天津0.0569158056792\n100.0526641740216\n开优步0.0526641740216\n事儿0.048554456767\n李师傅0.0485035501943\n天津人0.0482653686026\n绕路0.0478244723097\n出租车0.0448480260748\n时候0.0440840298591\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E我看了一下，觉得关键词提取还是比较靠谱的。当然，其中也混入了个数字10，好在无伤大雅。\u003C\u002Fp\u003E\n\u003Cp\u003E如果你需要修改关键词数量，就需要指定\u003Ccode\u003EtopK\u003C\u002Fcode\u003E参数。例如你要输出10个关键词，可以这样执行：\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccodeclass=\"python\"\u003Eforkeyword,weightinextract_tags(data,topK=10,withWeight=True):\nprint('%s%s'%(keyword,weight))\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E下面我们尝试另一种关键词提取方式——TextRank。\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccodeclass=\"python\"\u003Eforkeyword,weightintextrank(data,withWeight=True):\nprint('%s%s'%(keyword,weight))\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E关键词提取结果如下：\u003C\u002Fp\u003E\n\u003Cpre\u003E\u003Ccode\u003E优步1.0\n司机0.749405996648\n乘客0.594284506457\n姐姐0.485458741991\n天津0.451113490366\n目的地0.429410027466\n时候0.418083863303\n作者0.416903838153\n没有0.357764515052\n活儿0.291371566494\n上车0.277010013884\n绕路0.274608592084\n转载0.271932903186\n出来0.242580745393\n出租0.238639889991\n事儿0.228700322713\n单数0.213450680366\n出租车0.212049665481\n拉门0.205816713637\n跟着0.20513470986\n\u003C\u002Fcode\u003E\u003C\u002Fpre\u003E\n\u003Cp\u003E注意这次提取的结果，与TF-idf的结果有区别。至少，那个很突兀的“10”不见了。\u003C\u002Fp\u003E\n\u003Cp\u003E但是，这是不是意味着TextRank方法一定优于TF-idf呢？\u003C\u002Fp\u003E\n\u003Cp\u003E这个问题，留作思考题，希望在你认真阅读了后面的原理部分之后，能够独立做出解答。\u003C\u002Fp\u003E\n\u003Cp\u003E如果你只需要应用本方法解决实际问题，那么请跳过原理部分，直接看讨论吧。\u003C\u002Fp\u003E\n\u003Ch1\u003E原理\u003C\u002Fh1\u003E\n\u003Cp\u003E我们简要讲解一下，前文出现的2种不同关键词提取方式——TF-idf和TextRank的基本原理。\u003C\u002Fp\u003E\n\u003Cp\u003E为了不让大家感到枯燥，这里咱们就不使用数学公式了。后文我会给出相关的资料链接。如果你对细节感兴趣，欢迎按图索骥，查阅学习。\u003C\u002Fp\u003E\n\u003Cp\u003E先说TF-idf。\u003C\u002Fp\u003E\n\u003Cp\u003E它的全称是TermFrequency-inversedocumentfrequency。中间有个连字符，左右两侧各是一部分，共同结合起来，决定某个词的重要程度。\u003C\u002Fp\u003E\n\u003Cp\u003E第一部分，就是词频（TermFrequency），即某个词语出现的频率。\u003C\u002Fp\u003E\n\u003Cp\u003E我们常说“重要的事说三遍”。\u003C\u002Fp\u003E\n\u003Cp\u003E同样的道理，某个词语出现的次数多，也就说明这个词语重要性\u003Cstrong\u003E可能会\u003C\u002Fstrong\u003E很高。\u003C\u002Fp\u003E\n\u003Cp\u003E但是，这只是可能性，并不绝对。\u003C\u002Fp\u003E\n\u003Cp\u003E例如现代汉语中的许多虚词——“的，地，得”，古汉语中的许多句尾词“之、乎、者、也、兮”，这些词在文中可能出现许多次，但是它们显然不是关键词。\u003C\u002Fp\u003E\n\u003Cp\u003E这就是为什么我们在判断关键词的时候，需要第二部分（idf）配合。\u003C\u002Fp\u003E\n\u003Cp\u003E逆文档频率（inversedocumentfrequency）首先计算某个词在各文档中出现的频率。假设一共有10篇文档，其中某个词A在其中10篇文章中都出先过，另一个词B只在其中3篇文中出现。请问哪一个词更关键？\u003C\u002Fp\u003E\n\u003Cp\u003E给你一分钟思考一下，然后继续读。\u003C\u002Fp\u003E\n\u003Cp\u003E公布答案时间到。\u003C\u002Fp\u003E\n\u003Cp\u003E答案是B更关键。\u003C\u002Fp\u003E\n\u003Cp\u003EA可能就是虚词，或者全部文档共享的主题词。而B只在3篇文档中出现，因此很有可能是个关键词。\u003C\u002Fp\u003E\n\u003Cp\u003E逆文档频率就是把这种文档频率取倒数。这样第一部分和第二部分都是越高越好。二者都高，就很有可能是关键词了。\u003C\u002Fp\u003E\n\u003Cp\u003ETF-idf讲完了，下面我们说说TextRank。\u003C\u002Fp\u003E\n\u003Cp\u003E相对于TF-idf，TextRank要显得更加复杂一些。它不是简单做加减乘除运算，而是基于图的计算。\u003C\u002Fp\u003E\n\u003Cp\u003E下图是原始文献中的示例图。\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:700px;max-height:496px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:70.94%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"1356\"data-height=\"962\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-0642fb0d702325e3.png\"data-original-width=\"1356\"data-original-height=\"962\"data-original-format=\"\"data-original-filesize=\"359107\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003Eimage.png\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003ETextRank首先会提取词汇，形成节点；然后依据词汇的关联，建立链接。\u003C\u002Fp\u003E\n\u003Cp\u003E依照连接节点的多少，给每个节点赋予一个初始的权重数值。\u003C\u002Fp\u003E\n\u003Cp\u003E然后就开始迭代。\u003C\u002Fp\u003E\n\u003Cp\u003E根据某个词所连接所有词汇的权重，重新计算该词汇的权重，然后把重新计算的权重传递下去。直到这种变化达到均衡态，权重数值不再发生改变。这与Google的网页排名算法PageRank，在思想上是一致的。\u003C\u002Fp\u003E\n\u003Cdivclass=\"image-package\"\u003E\n\u003Cdivclass=\"image-container\"style=\"max-width:630px;max-height:450px;\"\u003E\n\u003Cdivclass=\"image-container-fill\"style=\"padding-bottom:71.43%;\"\u003E\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-view\"data-width=\"630\"data-height=\"450\"\u003E\u003Cimgdata-original-src=\"\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-bbb90c082e80d896.png\"data-original-width=\"630\"data-original-height=\"450\"data-original-format=\"\"data-original-filesize=\"283181\"\u003E\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cdivclass=\"image-caption\"\u003Eimage.png\u003C\u002Fdiv\u003E\n\u003C\u002Fdiv\u003E\n\u003Cp\u003E根据最后的权重值，取其中排列靠前的词汇，作为关键词提取结果。\u003C\u002Fp\u003E\n\u003Cp\u003E如果你对原始文献感兴趣，请参考以下链接：\u003C\u002Fp\u003E\n\u003Cul\u003E\n\u003Cli\u003E\n\u003Cahref=\"http:\u002F\u002Fwww.emeraldinsight.com\u002Fdoi\u002Fabs\u002F10.1108\u002Feb026526\"target=\"_blank\"rel=\"nofollow\"\u003ETF-idf原始文献链接\u003C\u002Fa\u003E。\u003C\u002Fli\u003E\n\u003Cli\u003E\n\u003Cahref=\"http:\u002F\u002Fweb.eecs.umich.edu\u002F~mihalcea\u002Fpapers\u002Fmihalcea.emnlp04.pdf\"target=\"_blank\"rel=\"nofollow\"\u003ETextRank原始文献链接\u003C\u002Fa\u003E。\u003C\u002Fli\u003E\n\u003C\u002Ful\u003E\n\u003Ch1\u003E讨论\u003C\u002Fh1\u003E\n\u003Cp\u003E小结一下，本文探讨了如何用Python对中文文本做关键词提取。具体而言，我们分别使用了TF-idf和TextRank方法，二者提取关键词的结果可能会有区别。\u003C\u002Fp\u003E\n\u003Cp\u003E你做过中文关键词提取吗？使用的是什么工具？它的效果如何？有没有比本文更高效的方法？欢迎留言，把你的经验和思考分享给大家，我们一起交流讨论。\u003C\u002Fp\u003E\n\u003Cp\u003E喜欢请点赞。还可以微信关注和置顶我的公众号\u003Cahref=\"http:\u002F\u002Foejqwrqkh.bkt.clouddn.com\u002F2016-10-11-22-26-16.jpg\"target=\"_blank\"rel=\"nofollow\"\u003E“玉树芝兰”(nkwangshuyi)\u003C\u002Fa\u003E。\u003C\u002Fp\u003E\n\u003Cp\u003E如果你对数据科学感兴趣，不妨阅读我的系列教程索引贴《\u003Cahref=\"https:\u002F\u002Fwww.jianshu.com\u002Fp\u002F85f4624485b9\"target=\"_blank\"\u003E如何高效入门数据科学？\u003C\u002Fa\u003E》，里面还有更多的有趣问题及解法。\u003C\u002Fp\u003E\n","paid_type":"fbook_free","notebook_id":130182,"commentable":true,"reprintable":true,"first_shared_at":"2017-12-07T22:18:15.000+08:00","wordage":2968,"paid_content_accessible":false,"share_image_url":"http:\u002F\u002Fupload-images.jianshu.io\u002Fupload_images\u002F64542-2bc0d58a72fe345d.png","show_paid_comment_tips":false,"seq":84,"book":{"id":1150,"retail_price":0,"name":"玉树芝兰","seq_desc":true,"paid":false,"image_url":"https:\u002F\u002Fupload.jianshu.io\u002Fbook\u002Fimage\u002F53ae9b43-387d-4007-8a10-7a07fc93afed.jpg","public_notes_count":168},"prev_note":{"title":"如何用Markdown写论文？","slug":"b0ac7ae98100"},"next_note":{"title":"如何用Python读取开放数据？","slug":"424e1c65f424"},"user":{"id":64542,"slug":"7618ab4a30e4","nickname":"王树义","gender":1,"avatar":"https:\u002F\u002Fupload.jianshu.io\u002Fusers\u002Fupload_avatars\u002F64542\u002F53f3ed10904a.JPG","intro":"终身学习者、大学教师。稍微懂一点儿写作、演讲、Python和机器学习。欢迎微信关注并置顶我的公...","intro_compiled":"终身学习者、大学教师。稍微懂一点儿写作、演讲、Python和机器学习。欢迎微信关注并置顶我的公...","wordage":532926,"likes_count":12965,"badges":[{"text":"互联网优秀作者","intro_url":"http:\u002F\u002Fwww.jianshu.com\u002Fp\u002Fd1d89ed69098","image_url":"https:\u002F\u002Fupload.jianshu.io\u002Fuser_badge\u002Fd859cce6-5b76-421c-aa99-ebe102569be0","icon":"other"}],"user_ip_addr":"天津","vip":null,"liked_by_user":false,"liked_user":false},"distribution_more_earn_percent":24,"last_updated_at":1530175082,"activity_collection_slug":null},"gift":null,"stories2017":[],"yuesaiMothersDay":[],"badgeUserNotes":[],"collectionPopularNotes":{"page":1,"count":10,"noMore":false,"data":[]},"featuredComments":null},"campaign":{"rankList":[],"dayByDayUser":{"season":{},"user":{},"join_in":null,"today_finished":null,"days":null,"season_days":null,"rebirth_cards":[],"rebirth_cards_count":null},"dayByDayRewards":{"season":[],"total":[]},"helpCreator":null},"publication":{"data":null,"recWriters":{"data":[],"status":{"page":0,"count":4,"error":null,"noMore":false,"loading":false,"totalPage":0}}},"distribution":{"info":null,"buyers":{"data":[],"noMore":false,"page":1,"count":10}},"order":{"webPayOrder":null,"goods":null,"openid":""},"convos":{"desc":null,"read":null},"audio":{"fm":[]},"ruby":{"noticeDate":"","fp":0,"hoursTime":0,"rate":"","userfp":0,"author_fp":0,"balance":"","first_day":"","last_day":"","amount":"","user":{"avatar":""},"wallet":{"assets":{},"income":{},"ranking":{},"user":{}},"info":{"user":{},"exchanging_jsb":"","jsb_balance":"","today_ranking":0,"yesterday_ranking":0,"yesterday_balance":null,"lending_balance":null,"unconfirmed_balance":"","yesterday_time":"","lending_balance18":""},"myrankings":{"rankings":[]},"rankings":{"rankings":[]},"transactions":{"has_more":false,"transactions":[]},"shells":{"has_more":false,"transactions":[]},"borrow":{"has_more":false,"transactions":[]},"collectionsfp":"","voter":[],"voterusers":[],"collections":[],"notice":{"load":true},"jsd":{"has_more":false,"transactions":[]},"hoursUsers":[],"hoursNotes":[],"fprate":{}},"missions":{"books_count":"","promotions_count":"","tasks":[],"promotions":[],"books":[],"lending_fp_amount":"","finished_count":0,"cny":0,"cny_displayable":false,"new_banner":false},"flow":{"type":"","changing":"","func":"","sort":"","count":15,"data":{}},"liquidation":{"rate":null,"increment":0,"displayable":true,"marketOpened":false},"lottery":{"leftCount":0,"viewAdLeftCount":0,"drawCount":0,"waitTime":0,"prizeList":[],"requestErr":false,"luckyWinners":[]},"group":{"info":null,"latest":[],"latestState":{"noMore":false,"loading":false,"error":false,"nothing":false},"hot":[],"hotState":{"noMore":false,"loading":false,"error":false,"nothing":false},"best":[],"bestState":{"noMore":false,"loading":false,"error":false,"nothing":false},"gt":null,"gp":null},"ambassador":{"rewardWeek":[],"rate":{},"distributionsInfo":{"next_member_benefit_percent":{},"jsd_reward":{},"member_rewards":{},"distribution_rewards":{},"referrals_rewards":{}},"spreadDetail":{"transactions":[],"page":0,"more":true},"jsdRewards":{"transactions":[],"page":1,"total_pages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