{"id":362,"date":"2018-08-26T14:12:45","date_gmt":"2018-08-26T14:12:45","guid":{"rendered":"http:\/\/ai.intelligentonlinetools.com\/ml\/?p=362"},"modified":"2018-09-10T00:00:11","modified_gmt":"2018-09-10T00:00:11","slug":"ml-search-results-clustering-analysis","status":"publish","type":"post","link":"http:\/\/ai.intelligentonlinetools.com\/ml\/ml-search-results-clustering-analysis\/","title":{"rendered":"Text Mining Techniques for Search Results Clustering"},"content":{"rendered":"<div class=\"yvixj69ec0382c649a\" ><script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\n<!-- Text analytics techniques 728_90 horizontal top -->\n<ins class=\"adsbygoogle\"\n     style=\"display:inline-block;width:728px;height:90px\"\n     data-ad-client=\"ca-pub-3416618249440971\"\n     data-ad-slot=\"2926649501\"><\/ins>\n<script>\n(adsbygoogle = window.adsbygoogle || []).push({});\n<\/script><\/div><style type=\"text\/css\">\r\n.yvixj69ec0382c649a {\r\nmargin: 5px; padding: 0px;\r\n}\r\n@media screen and (min-width: 1201px) {\r\n.yvixj69ec0382c649a {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 993px) and (max-width: 1200px) {\r\n.yvixj69ec0382c649a {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 769px) and (max-width: 992px) {\r\n.yvixj69ec0382c649a {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 768px) and (max-width: 768px) {\r\n.yvixj69ec0382c649a {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (max-width: 767px) {\r\n.yvixj69ec0382c649a {\r\ndisplay: block;\r\n}\r\n}\r\n<\/style>\r\n<p><b>Text search box<\/b> can be found almost in every web based application that has text data. We use search feature when we are looking for customer data, jobs descriptions, book reviews or some other information.  Simple <b>keyword matching<\/b> can be enough in some small tasks. However when we have many results something better than keyword match would be very helpful. Instead of going through a lot of results we would get results grouped by topic with a nice summary of topics. It would allow to see information at first sight.<\/p>\n<p>In this post we will look in some machine learning algorithms, applications and frameworks that can analyze output of search function and provide useful additional information for search results.  <\/p>\n<h2>Machine Learning Clustering for Search Results<\/h2>\n<p><b>Search results clustering<\/b> problem is defined as an automatic, on-line grouping of similar documents in a search results list returned from a search engine. [1] <b>Carrot2<\/b> is the tool that was built to solve this problem.<br \/>\nCarrot2 is  Open Source Framework for building Search Results Clustering Engine. This tool can do search, cluster and visualize clusters. Which is very cool.  I was not able to find similar like this tool in the range of open source projects. If you are aware of such tool, please suggest in the comment box.<\/p>\n<p>Below are screenshots of clustering search results from Carrot2<\/p>\n<figure id=\"attachment_384\" aria-describedby=\"caption-attachment-384\" style=\"width: 515px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/carrot2-clustering-1.png\" alt=\"Clustering search results with Carrot2\" width=\"525\" height=\"285\" class=\"size-full wp-image-384\" srcset=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/carrot2-clustering-1.png 525w, http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/carrot2-clustering-1-300x163.png 300w\" sizes=\"(max-width: 525px) 100vw, 525px\" \/><figcaption id=\"caption-attachment-384\" class=\"wp-caption-text\">Clustering search results with Carrot2<\/figcaption><\/figure>\n<figure id=\"attachment_383\" aria-describedby=\"caption-attachment-383\" style=\"width: 690px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/aduna-cluster-map-visualization-clusters-20180829091106-e1535553163575.png\" alt=\"Aduna cluster map visualization clusters\" width=\"700\" height=\"414\" class=\"size-full wp-image-383\" \/><figcaption id=\"caption-attachment-383\" class=\"wp-caption-text\">Aduna cluster map visualization clusters with Carrot2<\/figcaption><\/figure>\n<p>The following algorithms are behind Carrot2 tool:<br \/>\nLingo algorithm constructs a \u201cterm-document matrix\u201d where each snippet gets a column, each word a row and the values are the frequency of that word in that snippet. It then applies a matrix factorization called singular value decomposition or SVD. [3]<\/p>\n<p>Suffix Tree Clustering (STC) uses the generalised suffix tree data structure, to efficiently build a list of the most frequently used phrases in the snippets from the search results. [3] <\/p>\n<h2>Topic modelling<\/h2>\n<p><b>Topic modelling<\/b> is another approach that is used to identify which topic is discussed in documents or text snippets provided by search function.  There are several methods like LSA, pLSA, LDA [11]<\/p>\n<p>Comprehensive overview of Topic Modeling and its associated techniques is described in [12]<\/p>\n<p>Topic modeling can be represented via below diagram. Our goal is identify topics given documents with the words<br \/>\n<figure id=\"attachment_385\" aria-describedby=\"caption-attachment-385\" style=\"width: 611px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/doc_topic_term_flow.png\" alt=\"Topic modeling diagram\" width=\"621\" height=\"256\" class=\"size-full wp-image-385\" srcset=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/doc_topic_term_flow.png 621w, http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/doc_topic_term_flow-300x124.png 300w\" sizes=\"(max-width: 621px) 100vw, 621px\" \/><figcaption id=\"caption-attachment-385\" class=\"wp-caption-text\">Topic modeling diagram<\/figcaption><\/figure><\/p>\n<p>Below is plate notation of LDA model.<br \/>\n<figure id=\"attachment_392\" aria-describedby=\"caption-attachment-392\" style=\"width: 660px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/Untitled-Diagram-2.png\" alt=\"Plate notation of LDA model\" width=\"670\" height=\"292\" class=\"size-full wp-image-392\" srcset=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/Untitled-Diagram-2.png 670w, http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/08\/Untitled-Diagram-2-300x131.png 300w\" sizes=\"(max-width: 670px) 100vw, 670px\" \/><figcaption id=\"caption-attachment-392\" class=\"wp-caption-text\">Plate notation of LDA model<\/figcaption><\/figure><\/p>\n<p>Plate notation representing the LDA model. [19]<br \/>\n\u03b1lpha is the parameter of the Dirichlet prior on the per-document topic distributions,<br \/>\n\u03b2eta is the parameter of the Dirichlet prior on the per-topic word distribution,<br \/>\np is the topic distribution for document m,<br \/>\nZ is the topic for the n-th word in document m, and<br \/>\nW is the specific word.<\/p>\n<p>We can use different NLP libraries (NLTK, spaCY, gensim, textacy) for topic modeling.<br \/>\nHere is the example of topic modeling with textacy python library:<br \/>\n<a href=\"https:\/\/ai.intelligentonlinetools.com\/ml\/topic-modeling-python-textacy\/\" target=\"_blank\">Topic Modeling Python and Textacy Example<\/a><\/p>\n<p>Here are examples of topic modeling with gensim library:<br \/>\n<a href=\"https:\/\/intelligentonlinetools.com\/blog\/2017\/01\/08\/topic-extraction-from-blog-posts-with-lsi-and-lda-and-python\/\" target=\"_blank\">Topic Extraction from Blog Posts with LSI , LDA and Python<\/a><br \/>\n<a href=\"https:\/\/intelligentonlinetools.com\/blog\/2017\/01\/22\/data-visualization-visualizing-an-lda-model-using-python\/\" target=\"_blank\">Data Visualization \u2013 Visualizing an LDA Model using Python<\/a><\/p>\n<h2>Using Word Embeddings<\/h2>\n<p>Word embeddings like gensim, word2vec, glove showed very good results in NLP and are widely used now. This is also used for search results clustering. The first step would be create model for example gensim. In the next step text data are converted to vector representation. Words embedding improve preformance by leveraging information on how words are semantically correlated to each other [7][10]<\/p>\n<h2>Neural Topic Model (NTM) and Other Approaches<\/h2>\n<p>Below are some other approaches that can be used for topic modeling for search results organizing.<br \/>\n<b>Neural topic modeling<\/b> &#8211; combines a neural network with a latent topic model. [14]<br \/>\n<b>Topic modeling with Deep Belief Nets<\/b> is described in [17]. The concept of the method is to load bag-of-words (BOW) and produce a strong latent representation that will then be used for a content based recommender system. The authors report that model outperform LDA, Replicated Softmax, and DocNADE models on document retrieval and document classification tasks.<\/p>\n<p>Thus we looked at different techniques for search results clustering. In the future posts we will implement some of them. What machine learning methods do you use for presenting search results? I would love to hear. <\/p>\n<p><strong>References<\/strong><br \/>\n1. <a href=\"https:\/\/www.researchgate.net\/publication\/2870958_Lingo_Search_Results_Clustering_Algorithm\" target=\"_blank\"> Lingo Search Results Clustering Algorithm <\/a><br \/>\n2. <a href=\"https:\/\/project.carrot2.org\/algorithms.html\" target=\"_blank\">Carrot2 Algorithms<\/a><br \/>\n3. <a href=\"https:\/\/www.indexdata.com\/clustering-snippets-carrot2\/\" target=\"_blank\">Carrot2<\/a><br \/>\n4. <a href=\"https:\/\/carrot2.github.io\/solr-integration-strategies\/carrot2-3.6.3\/index.html\" target=\"_blank\">   Apache SOLR and Carrot2 integration strategies<\/a><br \/>\n5. <a href=\"https:\/\/storage.googleapis.com\/pub-tools-public-publication-data\/pdf\/37745.pdf\" target=\"_blank\">  Topical Clustering of Search Results<\/a><br \/>\n6. <a href=\"http:\/\/www.python36.com\/k-means-clustering-for-text-dataset\/\" target=\"_blank\">K-means clustering for text dataset<\/a><br \/>\n7. <a href=\"https:\/\/www.kaggle.com\/sgunjan05\/document-clustering-using-doc2vec-word2vec\" target=\"_blank\">    Document Clustering using Doc2Vec\/word2vec<\/a><br \/>\n8 <a href=\"https:\/\/towardsdatascience.com\/automatic-topic-clustering-using-doc2vec-e1cea88449c\" target=\"_blank\">   Automatic Topic Clustering Using Doc2Vec<\/a><br \/>\n9. <a href=\"https:\/\/www.researchgate.net\/publication\/2870958_Lingo_Search_Results_Clustering_Algorithm\"  target=\"_blank\">   Search Results Clustering Algorithm<\/a><br \/>\n10. <a href=\"https:\/\/www.datacamp.com\/community\/tutorials\/lda2vec-topic-model\"  target=\"_blank\">  LDA2vec: Word Embeddings in Topic Models<\/a><br \/>\n11. <a href=\"https:\/\/towardsdatascience.com\/topic-modelling-in-python-with-nltk-and-gensim-4ef03213cd21\"  target=\"_blank\">Topic Modelling in Python with NLTK and Gensim<\/a><br \/>\n12. <a href=\"https:\/\/medium.com\/nanonets\/topic-modeling-with-lsa-psla-lda-and-lda2vec-555ff65b0b05\"  target=\"_blank\">Topic Modeling with LSA, PLSA, LDA &#038; lda2Vec<\/a><br \/>\n13. <a href=\"https:\/\/towardsdatascience.com\/text-summarization-with-amazon-reviews-41801c2210b\"  target=\"_blank\">  Text Summarization with Amazon Reviews <\/a><br \/>\n14. <a href=\"https:\/\/cs.nyu.edu\/~wanli\/wan-zhu-fergus12.pdf\" target=\"_blank\"> A Hybrid Neural Network-Latent Topic Model<\/a><br \/>\n15. <a href=\"https:\/\/github.com\/metinsay\/docluster\" target=\"_blank\">docluster<\/a><br \/>\n16. <a href=\"https:\/\/github.com\/larsmaaloee\/deep-belief-nets-for-topic-modeling\" target=\"_blank\">Deep Belief Nets for Topic Modeling<\/a><br \/>\n17. <a href=\"https:\/\/www.cs.toronto.edu\/~hinton\/absps\/deepBMdocs.pdf\">Modeling Documents with a Deep Boltzmann Machine<\/a><br \/>\n18. <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2016\/08\/beginners-guide-to-topic-modeling-in-python\/\" target=\"_blank\">Beginners guide to topic modeling in python<\/a><br \/>\n19. <a href=\"https:\/\/en.wikipedia.org\/wiki\/Latent_Dirichlet_allocation\" target=\"_blank\">Latent Dirichlet allocation<\/a><\/p>\n<div class=\"kvzoo69ec0382c64cd\" ><center>\n<script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\n<!-- Text analytics techniques link ads horizontal Medium after content -->\n<ins class=\"adsbygoogle\"\n     style=\"display:inline-block;width:468px;height:15px\"\n     data-ad-client=\"ca-pub-3416618249440971\"\n     data-ad-slot=\"5765984772\"><\/ins>\n<script>\n(adsbygoogle = window.adsbygoogle || []).push({});\n<\/script>\n\n<script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\n<ins class=\"adsbygoogle\"\n     style=\"display:block\"\n     data-ad-format=\"autorelaxed\"\n     data-ad-client=\"ca-pub-3416618249440971\"\n     data-ad-slot=\"3903486841\"><\/ins>\n<script>\n     (adsbygoogle = window.adsbygoogle || []).push({});\n<\/script>\n<\/center><\/div><style type=\"text\/css\">\r\n.kvzoo69ec0382c64cd {\r\nmargin: 5px; padding: 0px;\r\n}\r\n@media screen and (min-width: 1201px) {\r\n.kvzoo69ec0382c64cd {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 993px) and (max-width: 1200px) {\r\n.kvzoo69ec0382c64cd {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 769px) and (max-width: 992px) {\r\n.kvzoo69ec0382c64cd {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 768px) and (max-width: 768px) {\r\n.kvzoo69ec0382c64cd {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (max-width: 767px) {\r\n.kvzoo69ec0382c64cd {\r\ndisplay: block;\r\n}\r\n}\r\n<\/style>\r\n","protected":false},"excerpt":{"rendered":"<p>Text search box can be found almost in every web based application that has text data. We use search feature when we are looking for customer data, jobs descriptions, book reviews or some other information. Simple keyword matching can be enough in some small tasks. However when we have many results something better than keyword &#8230; <a title=\"Text Mining Techniques for Search Results Clustering\" class=\"read-more\" href=\"http:\/\/ai.intelligentonlinetools.com\/ml\/ml-search-results-clustering-analysis\/\" aria-label=\"More on Text Mining Techniques for Search Results Clustering\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[40],"tags":[39,41],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Text Mining Techniques for Search Results Clustering - Text Analytics Techniques<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/ai.intelligentonlinetools.com\/ml\/ml-search-results-clustering-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Text Mining Techniques for Search Results Clustering - Text Analytics Techniques\" \/>\n<meta property=\"og:description\" content=\"Text search box can be found almost in every web based application that has text data. 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