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dc.contributor.authorNurul Shahazira, Rosli
dc.contributor.authorMaira Madihah, Mohamed Azmee
dc.contributor.authorNur ‘Aisyah, Mohd Samsudin
dc.contributor.authorMuhammad Firdaus, Mustapha
dc.contributorFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Kelantan, Bukit Ilmu, 18500 Machang, Kelantan, Malaysiaen_US
dc.creatorMuhammad Firdaus, Mustapha
dc.date.accessioned2023-01-24T13:03:06Z
dc.date.available2023-01-24T13:03:06Z
dc.date.issued2022-12
dc.identifier.citationApplied Mathematics and Computational Intelligence (AMCI), vol.11(1), 2022, pages 360-372en_US
dc.identifier.issn2289-1315 (print)
dc.identifier.issn2289-1323 (online)
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/77706
dc.descriptionLink to publisher's homepage at https://amci.unimap.edu.my/en_US
dc.description.abstractUsers commonly provide feedback on certain applications. Users can provide either positive, negative or neutral reviews. To determine whether the reviews are positive, negative or neutral, this study use sentiment analysis through various methods of text mining and materials. In this study, a sentiment analysis application for TikTok analysis was conducted using RapidMiner. This project is conducted based on three issues from TikTok which are account review, sound review and video review. These issues are analyzed using Decision Tree, Naive Bayes and k-NN. RapidMiner is used throughout the process to ensure that the data is accurately performed. Then, the result is gathered by checking the accuracy of data based on the three methods. To analyze the data and obtain an exact performance of the outcome, the process of visualization and modelling is required. The analysis of the reviews from the users shows that majority reviews were positive compared to the negative and neutral reviews especially on video issue.en_US
dc.language.isoenen_US
dc.publisherInstitute of Engineering Mathematics, Universiti Malaysia Perlisen_US
dc.subject.otherRapidMineren_US
dc.subject.otherSentiment Analysisen_US
dc.subject.otherTikToken_US
dc.titleSentiment analysis on TikTok using RapidMineren_US
dc.typeArticleen_US
dc.identifier.urlhttps://amci.unimap.edu.my/
dc.contributor.urlmdfirdaus@uitm.edu.myen_US


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