A Comparative Analysis of Machine Learning Models in News Categorization

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> A Comparative Analysis of Machine Learning Models in News Categorization | U.Porto Journal of Engineering </title> <link rel="icon" href="https://journalengineering.fe.up.pt/public/journals/3/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.3"> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Mohammad Hossein Zolfagharnasab"/> <meta name="DC.Creator.PersonalName" content="Siavash Damari"/> <meta name="DC.Date.created" scheme="ISO8601" content="2024-07-10"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-11-22"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-07-10"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-07-10"/> <meta name="DC.Description" xml:lang="en" content="The constant stream of news nowadays highlights the necessity for meticulous assessment to ensure that the information accurately reaches its intended audience with the least amount of delay least delay. Despite the flexibility and efficiency of Deep Learning (DL) models, their intricate training and substantial resource demands pose significant challenges for their deployment in real-time applications. In this regard, this study evaluates the performance of resource-efficient Machine Learning (ML) techniques – Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) – in categorizing news. Based on the results, all the evaluated models attain a commendable level of accuracy in news categorization. Notably, the SVM excels, achieving an accuracy rate of 98% and a mean squared error of 0.28. This performance exemplifies the robust effectiveness of classical ML models in the categorization of news, particularly when enhanced by a suitably tailored preprocessing pipeline."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="2183-6493_0010-003_002464"/> <meta name="DC.Identifier.pageNumber" content="23-38"/> <meta name="DC.Identifier.DOI" content="10.24840/2183-6493_0010-003_002464"/> <meta name="DC.Identifier.URI" content="https://journalengineering.fe.up.pt/index.php/upjeng/article/view/2183-6493_0010-003_002464"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2024 Mohammad Hossein Zolfagharnasab, Siavash Damari"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0"/> <meta name="DC.Source" content="U.Porto Journal of Engineering"/> <meta name="DC.Source.ISSN" content="2183-6493"/> <meta name="DC.Source.Issue" content="3"/> <meta name="DC.Source.Volume" content="10"/> <meta name="DC.Source.URI" content="https://journalengineering.fe.up.pt/index.php/upjeng"/> <meta name="DC.Subject" xml:lang="en" content="Real-time Content Classification"/> <meta name="DC.Subject" xml:lang="en" content="News Categorization"/> <meta name="DC.Subject" xml:lang="en" content=" Natural Language Processing"/> <meta name="DC.Subject" xml:lang="en" content="Machine Learning"/> <meta name="DC.Title" content="A Comparative Analysis of Machine Learning Models in News Categorization"/> <meta name="DC.Type" content="Text.Serial.Journal"/> <meta name="DC.Type.articleType" content="PEC202324_01"/> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="U.Porto Journal of Engineering"/> <meta name="citation_journal_abbrev" content="UPjeng"/> <meta name="citation_issn" content="2183-6493"/> <meta name="citation_author" content="Mohammad Hossein Zolfagharnasab"/> <meta name="citation_author_institution" content="University of Porto, Faculty of Engineering. "/> <meta name="citation_author" content="Siavash Damari"/> <meta name="citation_author_institution" content="University of Allameh Tabataba'i, Department of Statistics, Mathematics, and Computer Science."/> <meta name="citation_title" content="A Comparative Analysis of Machine Learning Models in News Categorization"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2024/07/10"/> <meta name="citation_volume" content="10"/> <meta name="citation_issue" content="3"/> <meta name="citation_firstpage" content="23"/> <meta name="citation_lastpage" content="38"/> <meta name="citation_doi" content="10.24840/2183-6493_0010-003_002464"/> <meta name="citation_abstract_html_url" content="https://journalengineering.fe.up.pt/index.php/upjeng/article/view/2183-6493_0010-003_002464"/> <meta name="citation_abstract" xml:lang="en" content="The constant stream of news nowadays highlights the necessity for meticulous assessment to ensure that the information accurately reaches its intended audience with the least amount of delay least delay. Despite the flexibility and efficiency of Deep Learning (DL) models, their intricate training and substantial resource demands pose significant challenges for their deployment in real-time applications. In this regard, this study evaluates the performance of resource-efficient Machine Learning (ML) techniques – Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) – in categorizing news. Based on the results, all the evaluated models attain a commendable level of accuracy in news categorization. Notably, the SVM excels, achieving an accuracy rate of 98% and a mean squared error of 0.28. This performance exemplifies the robust effectiveness of classical ML models in the categorization of news, particularly when enhanced by a suitably tailored preprocessing pipeline."/> <meta name="citation_keywords" xml:lang="en" content="Real-time Content Classification"/> <meta name="citation_keywords" xml:lang="en" content="News Categorization"/> <meta name="citation_keywords" xml:lang="en" content=" Natural Language Processing"/> <meta name="citation_keywords" xml:lang="en" content="Machine Learning"/> <meta name="citation_pdf_url" content="https://journalengineering.fe.up.pt/index.php/upjeng/article/download/2183-6493_0010-003_002464/2183-6493_0010-003_002464"/> <link rel="alternate" type="application/atom+xml" href="https://journalengineering.fe.up.pt/index.php/upjeng/gateway/plugin/APP%5Cplugins%5Cgeneric%5CwebFeed%5CWebFeedGatewayPlugin/atom"> <link rel="alternate" type="application/rdf+xml" href="https://journalengineering.fe.up.pt/index.php/upjeng/gateway/plugin/APP%5Cplugins%5Cgeneric%5CwebFeed%5CWebFeedGatewayPlugin/rss"> <link rel="alternate" type="application/rss+xml" href="https://journalengineering.fe.up.pt/index.php/upjeng/gateway/plugin/APP%5Cplugins%5Cgeneric%5CwebFeed%5CWebFeedGatewayPlugin/rss2"> <link rel="stylesheet" href="https://journalengineering.fe.up.pt/index.php/upjeng/$$$call$$$/page/page/css?name=bootstrap" type="text/css" /> </head> <body class="pkp_page_article pkp_op_view has_site_logo"> <div class="pkp_structure_page"> <nav id="accessibility-nav" class="sr-only" role="navigation" aria-label="Quick jump to page content"> <ul> <li><a href="#main-navigation">Main Navigation</a></li> <li><a href="#main-content">Main Content</a></li> <li><a href="#sidebar">Sidebar</a></li> </ul> </nav> <header class="navbar navbar-default" id="headerNavigationContainer" role="banner"> <div class="container-fluid"> <div class="row"> <nav aria-label="User Navigation"> <ul id="navigationUser" class="nav nav-pills tab-list pull-right"> <li class=" menu-item-1"> <a href="https://journalengineering.fe.up.pt/index.php/upjeng/user/register"> Register </a> </li> <li class=" menu-item-2"> <a href="https://journalengineering.fe.up.pt/index.php/upjeng/login"> Login </a> </li> </ul> </nav> </div> </div> <div class="container-fluid"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#nav-menu" aria-expanded="false" aria-controls="nav-menu"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar"></span> <span class="icon-bar"></span> <span class="icon-bar"></span> </button> <div class="site-name"> <a href=" https://journalengineering.fe.up.pt/index.php/upjeng/index " class="navbar-brand navbar-brand-logo"> <img src="https://journalengineering.fe.up.pt/public/journals/3/pageHeaderLogoImage_en_US.png" alt="U. 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Despite the flexibility and efficiency of Deep Learning (DL) models, their intricate training and substantial resource demands pose significant challenges for their deployment in real-time applications. In this regard, this study evaluates the performance of resource-efficient Machine Learning (ML) techniques – <a></a>Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) – in categorizing news. Based on the results, all the evaluated models attain a commendable level of accuracy in news categorization. Notably, the SVM excels, achieving an accuracy rate of 98% and a mean squared error of 0.28. This performance exemplifies the robust effectiveness of classical ML models in the categorization of news, particularly when enhanced by a suitably tailored preprocessing pipeline.</p> </div> </div> <section class="item downloads_chart"> <h2 class="label"> Downloads </h2> <div class="value"> <canvas class="usageStatsGraph" data-object-type="Submission" data-object-id="2464"></canvas> <div class="usageStatsUnavailable" data-object-type="Submission" data-object-id="2464"> Download data is not yet available. </div> </div> </section> </section> <section class="article-more-details"> <h2 class="sr-only">Article Details</h2>   <div class="panel panel-default copyright"> <div class="panel-body"> <a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /></a><p>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 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<li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_blank">The Effect of Open Access</a>).</li> </ol> </div> </div> <div class="panel panel-default author-bios"> <div class="panel-heading"> Author Biographies </div> <div class="panel-body"> <div class="media biography"> <div class="media-body"> <h3 class="media-heading biography-author"> Mohammad Hossein Zolfagharnasab, <span class="affiliation">University of Porto, Faculty of Engineering. </span> </h3> <p>PhD student, Department of Electrical and Computer Engineering,</p> <p>Faculty of Engineering, University of Porto,</p> <p>Rua Dr. Roberto Frias, 4200-465 PORTO, Portugal</p> <p> </p> </div> </div> <div class="media biography"> <div class="media-body"> <h3 class="media-heading biography-author"> Siavash Damari, <span class="affiliation">University of Allameh Tabataba'i, Department of Statistics, Mathematics, and Computer Science.</span> </h3> <p>Master student,</p> <p>Department of Statistics, Mathematics, and Computer Science,</p> <p>University of Allameh Tabataba'i,</p> <p>Western Azadi Stadium Blvd, Tehran, Iran</p> </div> </div> </div> </div> </section> </div> </div> </article> </div> </main> </div> <footer class="footer" role="contentinfo"> <div class="container"> <div class="row"> <div class="col-md-10"> <p><small><a href="https://www.fe.up.pt" target="_blank" rel="noopener">Faculdade de Engenharia da Universidade do Porto</a><br /><a href="https://biblioteca.fe.up.pt" target="_blank" rel="noopener">Serviços de Documentação e Informação: Biblioteca</a><br />Rua Dr. Roberto Frias<br />4200-465 PORTO<br /><a title="Biblioteca da FEUP" href="mailto:biblioteca@fe.up.pt">biblioteca@fe.up.pt</a> |<a href="tel:+351220413805"> +351 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A Comparative Analysis of Machine Learning Models in News Categorization | U.Porto Journal of Engineering