{"id":176343,"date":"2024-01-07T18:49:04","date_gmt":"2024-01-07T17:49:04","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=176343"},"modified":"2024-01-07T18:49:24","modified_gmt":"2024-01-07T17:49:24","slug":"gensim-the-python-library-for-topic-modelling","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/gensim-the-python-library-for-topic-modelling","title":{"rendered":"Gensim: The Python library for topic modelling"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"176343\" class=\"elementor elementor-176343\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1af4fc0 e-flex e-con-boxed e-con\" data-id=\"1af4fc0\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e62567f elementor-widget elementor-widget-heading\" data-id=\"e62567f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Gensim is an open-source natural language processing (NLP) library in Python whose aim is to make topic modeling as accessible and efficient as possible.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6717ecf e-flex e-con-boxed e-con\" data-id=\"6717ecf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-348f163 elementor-widget elementor-widget-text-editor\" data-id=\"348f163\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>First, it&#8217;s important to understand what topic modeling is. It&#8217;s an &#8220;unsupervised&#8221; <a href=\"https:\/\/datascientest.com\/en\/cracking-the-code-on-underfitting-in-machine-learning\">Machine Learning<\/a> technique that automatically analyzes sets of texts to highlight the main topics.<\/p><p>How topic <strong>modelling works is fairly straightforward.<\/strong> It involves counting words and grouping word frames to deduce the theme within unstructured data.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ebafdfb e-flex e-con-boxed e-con\" data-id=\"ebafdfb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a68de5c elementor-widget elementor-widget-heading\" data-id=\"a68de5c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Gensim features<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2fb86ac elementor-widget elementor-widget-text-editor\" data-id=\"2fb86ac\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Gensim focuses on <strong>unsupervised learning<\/strong> and offers various functions and algorithms to handle the following tasks:<\/p><p><strong>Text pre-processing<\/strong> is an important step in preparing text data for analysis. This includes stopword removal, lemmatization,<strong> case normalization and frequent word removal.<\/strong><\/p><p>These functions clean up the textual data, making it easier to use.<\/p><p>For topic modeling, as mentioned earlier, the aim is to find themes in a set of texts. Gensim includes algorithms such as <strong>Latent Dirichlet Allocation (LDA)<\/strong> and Hierarchical Dirichlet Process (HDP).<\/p><p>Topic modeling is useful for analyzing large quantities of text, <strong>particularly in the fields of information retrieval and sentiment analysis.<\/strong><\/p><p>Semantic similarity is a measure of the semantic proximity between two texts or words.<\/p><p>Text classification is an <a href=\"https:\/\/datascientest.com\/en\/nlp-training-become-an-nlp-pro-and-master-the-art-of-natural-language-processing\">NLP technique<\/a> that classifies texts into predefined categories. Sentiment analysis, for example, classifies texts according to their emotional tone.<\/p><p>Information retrieval is an <a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\">NLP technique that finds relevant information<\/a> in a set of texts.<\/p><p>Gensim offers algorithms such as inverse indexing (which creates an index of all words in a set of texts) and term search (which finds texts containing a specific word or expression).<\/p><p><strong>Information retrieval<\/strong> is useful for analyzing large sets of texts, particularly in the field of business intelligence and social media analysis.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fd1cdae elementor-widget elementor-widget-image\" data-id=\"fd1cdae\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/nlp-gensim.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb95e83 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"cb95e83\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Mastering Gensim functionalities<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-76130a2 e-flex e-con-boxed e-con\" data-id=\"76130a2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6a5045b elementor-widget elementor-widget-heading\" data-id=\"6a5045b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The limits of Gensim<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0800348 elementor-widget elementor-widget-text-editor\" data-id=\"0800348\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Despite the importance of the tasks that can be carried out with<strong> Gensim,<\/strong> it&#8217;s important to understand its limitations. First and foremost, this library doesn&#8217;t provide enough tools to run an<strong> NLP<\/strong> project from start to finish. The use of another library, <a href=\"https:\/\/datascientest.com\/en\/spacy-nlps-open-source-python-library\">such as NLTK or spaCy<\/a>, is recommended.<\/p><p>Gensim was designed for unsupervised topic modeling, and will be less suited to topic classification.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-00e6619 e-flex e-con-boxed e-con\" data-id=\"00e6619\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-920917e elementor-widget elementor-widget-heading\" data-id=\"920917e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Why use Gensim?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a65098 elementor-widget elementor-widget-text-editor\" data-id=\"5a65098\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Gensim&#8217;s motto<\/strong> is &#8220;topic modelling for humans&#8221;. The aim of this library is to offer an easy-to-use, high-performance way of representing documents in semantic vectors.<\/p><p>One of Gensim&#8217;s great strengths lies in its ability to work with large datasets and to &#8220;process&#8221; streaming data. This allows the training corpus to reside partially on <strong>RAM.<\/strong><\/p><p>This library will run on all platforms (<strong>Windows, macOS, Linux<\/strong>) and has been developed to be as fast as possible for vector embedding.<\/p><p>What&#8217;s more, Gensim supports Deep Learning!<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d573981 e-flex e-con-boxed e-con\" data-id=\"d573981\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-bca9b32 elementor-widget elementor-widget-heading\" data-id=\"bca9b32\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-840c305 elementor-widget elementor-widget-text-editor\" data-id=\"840c305\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Gensim<\/strong> is an extremely powerful tool for subject modeling. Designed by professionals, this tool has been optimized to handle large datasets in a minimum of time.<\/p><p>Gensim&#8217;s vocation is not to carry out an <strong>NLP project in its entirety<\/strong>, but to concentrate on supervised learning. It will be possible to use it in conjunction with other <a href=\"https:\/\/datascientest.com\/en\/mastering-nltk-your-ultimate-guide-to-pythons-nlp-toolkit\"><strong>NLP libraries such as Spacy or NTLK.<\/strong><\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd6621e elementor-widget elementor-widget-text-editor\" data-id=\"cd6621e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Now that you know all about <strong>Gensim,<\/strong> if you want to learn how to use it, don&#8217;t hesitate to choose DataScientest&#8217;s Data Science training courses.<\/p><p>Each course includes a module dedicated to learning Python and its libraries.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-15300ab elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"15300ab\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover DataScientest's training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Gensim is an open-source natural language processing (NLP) library in Python whose aim is to make topic modeling as accessible and efficient as possible. First, it&#8217;s important to understand what topic modeling is. It&#8217;s an &#8220;unsupervised&#8221; Machine Learning technique that automatically analyzes sets of texts to highlight the main topics. How topic modelling works is [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":176344,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Gensim: The Python library for topic modelling<\/title>\n<meta name=\"description\" content=\"Explore the Power of Gensim to Uncover Hidden Patterns and Extract Insights from Your Text Data while making the...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/gensim-the-python-library-for-topic-modelling\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" 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