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A systematic review of 224 papers from 2003 to 2022 analyzes these differences across six aspects: datasets, models, best accuracy reporting, performance metrics, train/test splitting methods, and comparative approaches, while also highlighting limitations and research gaps.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/twenty-years-of-machine-learning-based-text-classification-a-systematic-review/290478/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/twenty-years-of-machine-learning-based-text-classification-a-systematic-review/290478.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What tasks does machine-learning-based text classification support?","Question",{"text":112,"@type":113},"It supports spam detection, hate speech identification, reviews and rating summarization, sentiment analysis, and topic modeling.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How many papers and what time span were included in the systematic review?",{"text":117,"@type":113},"The review surveyed 224 papers published between 2003 and 2022.",{"name":119,"@type":110,"acceptedAnswer":120},"Which six aspects does the paper use to analyze differences across the literature?",{"text":121,"@type":113},"It analyzes differences by datasets, machine learning models, best accuracy, performance evaluation metrics, training/testing splitting methods, and comparisons among models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290478,1789641783,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","algorithms  \nArticle  \nTwenty Years of Machine-Learning-Based Text Classification: A Systematic Review  \nAshokkumar Palanivinayagam 1, Claude Ziad El-Bayeh 2 and Robertas Damaševiˇcius 3, *  \nCitation: Palanivinayagam, A.;  \nEl-Bayeh, C.Z.; Damaševiˇcius, R. Twenty Years of MachineLearning-Based Text Classification: A Systematic Review. Algorithms 2023, 16, 236. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)a16050236  \nAcademic Editor: Frank Werner  \nReceived: 30 December 2022  \nRevised: 19 April 2023  \nAccepted: 27 April 2023  \nPublished: 29 April 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India  \n2 Department of Electrical Engineering, Bayeh Institute, Amchit 4307, Lebanon  \n3 Department of Software Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania  \n* Correspondence: [robertas.damasevicius@ktu.lt](robertas.damasevicius@ktu.lt)  \nAbstract: Machine-learning-based text classification is one of the leading research areas and has a wide range of applications, which include spam detection, hate speech identification, reviews, rating summarization, sentiment analysis, and topic modelling. Widely used machine-learning-based research differs in terms of the datasets, training methods, performance evaluation, and comparison methods used. In this paper, we surveyed 224 papers published between 2003 and 2022 that employed machine learning for text classification. The Preferred Reporting Items for Systematic Reviews (PRISMA) statement is used as the guidelines for the systematic review process. The comprehensive differences in the literature are analyzed in terms of six aspects: datasets, machine learning models, best accuracy, performance evaluation metrics, training and testing splitting methods, and comparisons among machine learning models. Furthermore, we highlight the limitations and research gapsin the literature. Although the research works included in the survey perform well in terms of text classification, improvement is required in many areas. We believe that this survey paper will be useful for researchers in the field of text classification.  \nKeywords: machine learning; text classification; natural language processing; spam detection; sentiment analysis; rating summarization  \n1. Introduction  \nMachine learning models provide the best alternative to traditional methods in the field of text classification. Text-classification-based research has been conducted extensively in recent years to improve the performance of machine learning models [1] . Text classification can be done manually, but it is time-consuming and has a high cost. Manual classification comes with lots of errors and is less accurate because of human error and a lack of domain knowledge understanding. There was a huge revolution in text classification when machine learning models, such as the Support Vector Machines (SVM), Naive Bayes (NB), and Random Forest (RF) started to replace manual work, because these models not only reduce the time and cost but also are highly accurate for classification [2] . Since the inception of machine learning models, numerous studies have been conducted to enhance, optimize, and refine the text classification process.  \nWith the recent increase in demand for various Natural Language Processing (NLP) technologies, such as chatbots [3], content classification [4], Sentiment Analysis [5–7], hate speech detection [8,9], authorship recognition and attribution [10], product and service recommenders [11,12], text summarization [13,14], email spam det","cbCaifJviy5vycCz","https://ap.wps.com/l/cbCaifJviy5vycCz","pdf",5375483,28,"English","# Introduction\n## Text classification workflow\n## Applications of machine learning models\n# Systematic review scope and analysis framework\n## Datasets, models, metrics, and comparisons\n## Limitations and research gaps","[{\"question\":\"What tasks does machine-learning-based text classification support?\",\"answer\":\"It supports spam detection, hate speech identification, reviews and rating summarization, sentiment analysis, and topic modeling.\"},{\"question\":\"How many papers and what time span were included in the systematic review?\",\"answer\":\"The review surveyed 224 papers published between 2003 and 2022.\"},{\"question\":\"Which six aspects does the paper use to analyze differences across the literature?\",\"answer\":\"It analyzes differences by datasets, machine learning models, best accuracy, performance evaluation metrics, training/testing splitting methods, and comparisons among models.\"}]","Twenty Years of Machine-Learning-Based Text Classification: A Systematic Review | PDF",71]