[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126456-en":3,"doc-seo-126456-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126456,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Advancing Sentiment Analysis during the Era of Data-Driven Exploration via the Implementation of Machine Learning Principles - Research Article","Rapid growth in digital communication has made social media and networked platforms essential sources of opinions and key information for analysis and decision support. This study investigates sentiment polarity analysis using the Naive Bayes supervised machine learning approach. Training data are grouped into small, medium, and large sets, with positive and negative dictionaries, followed by testing with two dataset categories. Performance is assessed via accuracy, precision, recall, and F1 across positive and negative review categories. Results confirm Naive Bayes achieves high accuracy efficiently and produces reliable sentiment classification.","BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, Vol. 12, No. 1, March 2024  \nResearch Article  \nAdvancing Sentiment Analysis during the Era of Data-Driven Exploration via the Implementation of Machine Learning Principles  \nAli A. H. Karah Bash and Ergun Ercelebi  \nAbstract— As a result of the rapid expansion in digital communication, information technology has seamlessly integrated into our everyday existence. It's nearly inconceivable to envision life without the presence of social media. The modern era of communications and networks encompasses not just entertainment instruments, but also contemporary means for users to share crucial data, viewpoints, and concepts. Certain data and information are of such significance that they are vital for analysis and the extraction of essential data that can subsequently be employed in decision support systems. This study examines sentiment polarity analysis through the utilization of the Naive Bayes approach. Naive Bayes is a supervised machine learning model employed for the prediction and analysis of data obtained from external sources. In the training phase, the dataset is categorized into three different groups: small, medium, and large. Additionally, both positive and negative dictionaries are obtained. As for the testing phase, two dataset categories are employed. To gauge the performance of the Naive Bayes algorithm in sentiment analysis, evaluation metrics like accuracy, precision, recall, and the F1 score are utilized. These assessment metrics are computed across three varied categories of positive and negative reviews. The experimental outcomes proved that the Naive Bayes approach is superior and the most effective technique for sentiment analysis.  \nBased on the findings, it can be stated that the Naive Bayes classifier delivers a high level of accuracy when analyzing the positive and negative polarity of the data. Additionally, this method requires less time to generate high-quality results.  \nIndex Terms— Sentiment Analysis, Favorable Polarity, Unfavorable Polarity, Naive Bayes Technique, Machine Learning, Guided Training.  \nI. INTRODUCTION  \nTHE sentiment analysis is not a recent domain; rather, its  \nexamination and evolution commenced during the early  \nAli A. H. Karah Bash, Department of Electric and Electronic Engineering, Hasan kalyoncu University, Gaziantep 27310, Turkey, (e-mail: [ali_karabash2016@yahoo.com](ali_karabash2016@yahoo.com)).  \n[https://orcid.org/0000-0002-6513-9180](https://orcid.org/0000-0002-6513-9180)  \nErgun Ercelebi, Department of Electric and Electronic Engineering, Gaziantep University, Gaziantep 27310, Turkey, (e-mail: [ergun.erceleb@gmail.com](ergun.erceleb@gmail.com)).  \n[https://orcid.org/0000-0002-4289-7026](https://orcid.org/0000-0002-4289-7026)  \nManuscript received August 09, 2023; accepted October 28, 2023. DOI: 10.17694/bajece.1340321  \n1990s and persists into the present [1,2] . The ongoing scholarly attention dedicated to this realm underscores its significance within our societal landscape, where sentiment analysis has emerged as a prevalent tool within networks and communication systems. Its application extends to the scrutiny of data, facilitating the extraction of novel insights utilized for analytical and statistical purposes [3] .  \nRecent investigations have extended beyond the boundaries of individual sentiment analysis methodologies, combining machine learning and artificial intelligence to examine a variety of data sources. These investigations delve into the field of predictive analysis, leveraging the subject matter of artificial intelligence and machine learning models to predict outcomes with high accuracy [4] .  \nFurthermore, sentiment analysis techniques are used to unpack the complexities of textual content sourced from communication systems and the broad reach of the Internet. This results in new data containing texts rich in intrinsic informational value, which then becomes raw material for machine learning alg","cbCaiaGjcq0f2OYx","https://ap.wps.com/l/cbCaiaGjcq0f2OYx","pdf",958022,5,1,9,"English","en",105,"# Introduction\n# Background","[{\"question\":\"What sentiment analysis method is evaluated in the study?\",\"answer\":\"The study evaluates the Naive Bayes supervised machine learning approach for sentiment polarity analysis.\"},{\"question\":\"How are the datasets prepared for training and testing?\",\"answer\":\"Training uses three dataset groups (small, medium, large) and derives positive and negative dictionaries, while testing uses two dataset categories.\"},{\"question\":\"Which metrics are used to measure the Naive Bayes performance?\",\"answer\":\"Accuracy, precision, recall, and the F1 score are used to evaluate performance across positive and negative review categories.\"}]","Advancing Sentiment Analysis during the Era of Data-Driven Exploration via the Implementation of Machine Learning Principles - Research Article | PDF",1785905151,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"advancing-sentiment-analysis-during-the-era-of-data-driven-exploration-via-the-implementation-of-machine-learning-principles-research-article","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/advancing-sentiment-analysis-during-the-era-of-data-driven-exploration-via-the-implementation-of-machine-learning-principles-research-article/126456/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What sentiment analysis method is evaluated in the study?","Question",{"text":77,"@type":78},"The study evaluates the Naive Bayes supervised machine learning approach for sentiment polarity analysis.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How are the datasets prepared for training and testing?",{"text":82,"@type":78},"Training uses three dataset groups (small, medium, large) and derives positive and negative dictionaries, while testing uses two dataset categories.",{"name":84,"@type":75,"acceptedAnswer":85},"Which metrics are used to measure the Naive Bayes performance?",{"text":86,"@type":78},"Accuracy, precision, recall, and the F1 score are used to evaluate performance across positive and negative review categories.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":20,"slug":138},19,"General","general"]