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   Text Mining, Analytics & More: All About Stop Words for Text Mining and Information Retrieval
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Blogger Template Style
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URL:      tinachen.org
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class=fauxborder-right></div><div class=fauxcolumn-inner></div></div><div class=cap-bottom><div class=cap-left></div><div class=cap-right></div></div></div><div class=columns-inner><div class=column-center-outer><div class=column-center-inner><div class="main section"id=main name=Main><div class="widget Blog"data-version=1 id=Blog1><div class="blog-posts hfeed"><div class=date-outer><h2 class=date-header><span> Oct 6, 2014 </span></h2><div class=date-posts><div class=post-outer><div class="post hentry uncustomized-post-template"itemprop=blogPost itemscope itemtype=http://schema.org/BlogPosting><meta content=1693476883305820976 itemprop=blogId><meta content=3776301983189182938 itemprop=postId><a name=3776301983189182938> </a><h3 class="post-title entry-title"itemprop=name>All About Stop Words for Text Mining and Information Retrieval</h3><div class=post-header><div class=post-header-line-1></div></div><div class="post-body entry-content"id=post-body-3776301983189182938 itemprop=articleBody><div style="text-align: left;"dir=ltr trbidi=on><h2 style="text-align: left;"><span style="color: #741b47; font-size: large;"> What are Stop Words? </span></h2><div><span style="color: #741b47; font-size: large;"> This article has moved to:  </span><a href=https://kavita-ganesan.com/what-are-stop-words/#.XddSCzJKhhE> https://kavita-ganesan.com/what-are-stop-words/#.XddSCzJKhhE </a></div><div><br></div><div style="text-align: justify;"><span style='font-family: "verdana" , sans-serif;'> When working with text mining applications, we often hear of the term “stop words” or "stop word list" or even "stop list". Stop words are basically a set of commonly used words in any language, not just English. The reason why stop words are critical to many applications is that, if we remove the words that are very commonly used in a given language, we can focus on the important words instead. For example, in the context of a search engine, if your search query is “how to develop information retrieval applications”, If the search engine tries to find web pages that contained the terms “how”, “to” “develop”, “information”, ”retrieval”, “applications” the search engine is going to find a lot more pages that contain the terms “how”, “to” than pages that contain information about developing information retrieval applications because the terms “how” and “to” are so commonly used in the English language. So, if we disregard these two terms, the search engine can actually focus on retrieving pages that contain the keywords: “develop” “information” “retrieval” “applications” – which would more closely bring up pages that are really of interest. This is just the basic intuition for using stop words. Stop words can be used in a whole range of tasks and these are just a few: </span></div><ol style="text-align: left;"><li><span style='font-family: "verdana" , sans-serif;'> <b> Supervised machine learning </b> – removing stop words from the feature space </span><li><span style='font-family: "verdana" , sans-serif;'> <b> Clustering </b> – removing stop words prior to generating clusters </span><li><span style='font-family: "verdana" , sans-serif;'> <b> Information retrieval </b> – preventing stop words from being indexed  </span><li><span style='font-family: "verdana" , sans-serif;'> <b> Text summarization </b> - excluding stop words from contributing to summarization scores & removing stop words when computing ROUGE scores </span></ol><h2 style="text-align: left;"><span style="color: #741b47; font-size: large;"> Types of Stop Words </span></h2><div><div><span style='font-family: "verdana" , sans-serif;'> Stop words are generally thought to be a <b> “single set of words” </b> . It really can mean different things to different applications. For example, in some applications removing all stop words right from determiners (e.g. the, a, an) to prepositions (e.g. above, across, before) to some adjectives (e.g. good, nice) can be an appropriate stop word list. To some applications however, this can be detrimental. For instance, in sentiment analysis removing adjective terms such as ‘good’ and ‘nice’ as well as negations such as ‘not’ can throw algorithms off their tracks. In such cases, one can choose to use a minimal stop list consisting of just determiners or determiners with prepositions or just coordinating conjunctions depending on the needs of the application. <br> <br> <b> Examples of minimal stop word lists that you can use: </b> </span><br><ul style="text-align: left;"><li><span style='font-family: "verdana" , sans-serif;'> <b> Determiners </b> - Determiners tend to mark nouns where a determiner usually will be followed by a noun <br> <i> examples: the, a, an, another </i> </span><li><span style='font-family: "verdana" , sans-serif;'> <b> Coordinating conjunctions </b> – Coordinating conjunctions connect words, phrases, and clauses <br> <i> examples: for, an, nor, but, or, yet, so </i> </span><li><span style='font-family: "verdana" , sans-serif;'> <b> Prepositions </b> - Prepositions express temporal or spatial relations <br> <i> examples: in, under, towards, before </i> </span></ul><span style='font-family: "verdana" , sans-serif;'> <br> In some domain specific cases, such as clinical texts, we may want a whole different set of stop words. For example, terms like “mcg” “dr” and “patient” may have less discriminating power in building intelligent applications compared to terms such as ‘heart’ ‘failure’ and ‘diabetes’. In such cases, we can also construct domain specific stop words as opposed to using a published stop word list. </span><br><span style='font-family: "verdana" , sans-serif;'> </span><h2 style="text-align: left;"><span style="color: #741b47; font-size: large;"> What About Stop Phrases? </span></h2><div style="text-align: justify;"><span style='font-family: "verdana" , sans-serif;'> Stop phrases are just like stop words just that instead of removing individual words, you exclude phrases. For example, if the phrase "good item" appears very frequently in your text but has a very low discriminating power or results in unwanted behavior in your results, one may choose to add such phrases as stop phrases. It is certainly possible to construct "stop phrases" the same way you construct stop words. For example, you can treat phrases with very low occurrence in your corpora as stop phrases. Similarly, you can consider phrases that occur in almost every document in your corpora as a stop phrase.  </span></div><span style='font-family: "verdana" , sans-serif;'> <br> </span></div><div><span style='font-family: "verdana" , sans-serif;'> </span><br><h2 style="text-align: left;"><span style="color: #741b47; font-size: large;"> Published Stop Word Lists </span></h2><span style='font-family: "verdana" , sans-serif;'> If you want to use stop words lists that have been published here are a few that you could use: </span><br><ul style="text-align: left;"><li><span style='font-family: "verdana" , sans-serif;'> <b> <a href=http://snowball.tartarus.org/algorithms/english/stop.txt> Snowball stop word </a> </b> list – this stop word list is published with the Snowball Stemmer  </span><li><span style='font-family: "verdana" , sans-serif;'> <b> <a href=https://bitbucket.org/kganes2/text-mining-resources/downloads> Terrier stop word </a> </b> list – this is a pretty comprehensive stop word list published with the Terrier package. </span><li><span style='font-family: "verdana" , sans-serif;'> <a href=https://bitbucket.org/kganes2/text-mining-resources/downloads> <b> Minimal stop word </b> </a> list – this is a stop word list that I compiled consisting of determiners, coordinating conjunctions and prepositions </span><li><a style="font-family: Verdana, sans-serif;"href=http://www.text-analytics101.com/2014/10/constructing-domain-specific-stop-word.html> <b> Construct your own stop word list </b> </a> <span style='font-family: "verdana" , sans-serif;'> – this article basically outlines an automatic method for constructing a stop word list for your specific data set (e.g. tweets, clinical texts, etc) </span></ul></div><div><h2><span style="color: #741b47; font-size: large;"> Constructing Domain Specific Stop Word Lists </span></h2><span style='font-family: "verdana" , sans-serif; text-align: justify;'> While it is fairly easy to use a published set of stop words, in many cases, using such stop words is completely insufficient for certain applications. For example, in clinical texts, terms like “mcg” “dr.” and “patient” occur almost in every document that you come across. So, these terms may be regarded as potential stop words for clinical text mining and retrieval. Similarly, for tweets, terms like “#” “RT”, “@username” can be potentially regarded as stop words. The common language specific stop word list generally DOES NOT cover such domain specific terms. Here is an article that I wrote that talks about how to construct </span><a style="font-family: Verdana, sans-serif; text-align: justify;"href=http://www.text-analytics101.com/2014/10/constructing-domain-specific-stop-word.html> <b> domain specific stop word lists </b> </a><span style='font-family: "verdana" , sans-serif; text-align: justify;'> . </span><span style='font-family: "verdana" , sans-serif;'> </span><br><span style='font-family: "verdana" , sans-serif;'> </span><br><div style="text-align: justify;"><span style='font-family: "verdana" , sans-serif;'> <br> </span></div><span style='font-family: "verdana" , sans-serif;'> </span><br><div><b> <span style='font-family: "verdana" , sans-serif;'> [  </span> <a style="font-family: Verdana, sans-serif;"href=https://bitbucket.org/kganes2/text-mining-resources/downloads/A%20Brief%20Note%20on%20Stop%20Words%20for%20Text%20Mining%20and%20Retrieval.pdf target=_blank> download article pdf </a> <span style='font-family: "verdana" , sans-serif;'>  ] </span> </b></div></div></div></div><div style="clear: both;"></div></div><div class=post-footer><div class="post-footer-line post-footer-line-1"><span class="post-author vcard"> </span><span class=post-timestamp> at <meta content=http://text-analytics101.rxnlp.com/2014/10/all-about-stop-words-for-text-mining.html itemprop=url> <a title="permanent link"class=timestamp-link href=http://text-analytics101.rxnlp.com/2014/10/all-about-stop-words-for-text-mining.html rel=bookmark> <abbr class=published itemprop=datePublished title=2014-10-06T13:29:00-07:00> October 06, 2014 </abbr> </a> </span><span class=post-comment-link> </span><span class=post-icons> </span><div class="post-share-buttons goog-inline-block"><a class="goog-inline-block share-button sb-email"title="Email This"href=https://www.blogger.com/share-post.g?blogID=1693476883305820976&postID=3776301983189182938&target=email target=_blank> <span class=share-button-link-text> Email This </span> </a><a 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