Publication:
Sentiment Analysis on Mixed-Language Social Media Post

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dc.citedby0
dc.contributor.authorMohamed Salleh F.H.en_US
dc.contributor.authorGorment N.Z.en_US
dc.contributor.authorMohd Ridza M.H.en_US
dc.contributor.authorid58883881800en_US
dc.contributor.authorid57201987388en_US
dc.contributor.authorid58883798500en_US
dc.date.accessioned2024-10-14T03:19:29Z
dc.date.available2024-10-14T03:19:29Z
dc.date.issued2023
dc.description.abstractSentiment analysis is a powerful tool that can be used by businesses and organizations to gather valuable data about public opinion towards a brand, product, topic, event, and much more. However, most Malaysians post on social media using a mix of English and Malay words, also known as Manglish, which are not catered to by existing sentiment analysis models. Malaysian-centric companies that are interested to analyze the Malaysian posts would have to do so manually, which is costly and time-consuming. Motivated by this issue, this paper aims to propose a method of performing sentiment analysis on posts using the power of machine learning. Several machine learning algorithms were identified and trained to classify a Manglish post as either positive or negative. Steps are also taken to ensure the reliability of the model and to improve it after the first training experiment. We found that this method is successful in producing a model that can predict social media post sentiment with reliable and acceptable accuracy. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICOCO59262.2023.10397971
dc.identifier.epage425
dc.identifier.scopus2-s2.0-85184854834
dc.identifier.spage420
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85184854834&doi=10.1109%2fICOCO59262.2023.10397971&partnerID=40&md5=8ec24a22f5321ccb307f4a5c44e8d6a5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34395
dc.pagecount5
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2023 IEEE International Conference on Computing, ICOCO 2023
dc.subjectmachine learning
dc.subjectmixed-language
dc.subjectsentiment analysis
dc.subjectsocial media post
dc.subjectLearning algorithms
dc.subjectMachine learning
dc.subjectSocial aspects
dc.subjectSocial networking (online)
dc.subjectAnalysis models
dc.subjectMachine-learning
dc.subjectMalaysians
dc.subjectMixed-language
dc.subjectPower
dc.subjectPublic opinions
dc.subjectSentiment analysis
dc.subjectSocial media
dc.subjectSocial medium post
dc.subjectTopic events
dc.subjectSentiment analysis
dc.titleSentiment Analysis on Mixed-Language Social Media Posten_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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