APPLYING OPINION MINING ALGORITHMS TO ANALYZE THE USER SENTIMENTS FROM USER REVIEWS TO RATE THE RESTAURANTS IN SULTANATE OF OMAN

Humans now spend the bulk of their time on the Internet, thanks to the huge rise of social media and other applications. Organizations and individuals have a shared platform to express their thoughts and ideas on any entity in the globe. Every website has a massive amount of information about produc...

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Bibliographic Details
Published inInternational Journal of Engineering Science Technologies Vol. 6; no. 1; pp. 186 - 192
Main Authors Joseph, Christine Mariam, T.A., Arunima, Sherimon, Vinu, Ali Al Amri, Khoula Mohamed Mubarak, Al Amri, Nidhal Ali Salim
Format Journal Article
LanguageEnglish
Published 15.02.2022
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ISSN2456-8651
2456-8651
DOI10.29121/ijoest.v6.i1.2022.263

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Summary:Humans now spend the bulk of their time on the Internet, thanks to the huge rise of social media and other applications. Organizations and individuals have a shared platform to express their thoughts and ideas on any entity in the globe. Every website has a massive amount of information about products and services. Through social media tools, we, as humans, may remark on any subject or incident. Most of the time, these public opinions are considered by organizations and individuals to better corporate operations and strategies, or in the decision-making process. Although these user opinions are practical in decision-making processes, manually analyzing and summarizing them is a difficult effort. It is a lengthy process. As a result, having automated opinion mining techniques to analyze user sentiments is critical. Since the beginning of social media, sentiment analysis has been a hot research area. Many studies have been conducted in various fields. The tourism sector is highlighted as one of the important topics for diversification in Oman Vision 2040. This study suggests an analysis of user reviews of Omani restaurants, categorizing them as positive or bad, and ranking the restaurants according to the reviews. The reviews are analyzed using opinion mining methods
ISSN:2456-8651
2456-8651
DOI:10.29121/ijoest.v6.i1.2022.263