Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICTAI.2013.62
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dc.titleAspect-based Twitter sentiment classification
dc.contributor.authorLek, H.H.
dc.contributor.authorPoo, D.C.C.
dc.date.accessioned2014-07-04T03:11:35Z
dc.date.available2014-07-04T03:11:35Z
dc.date.issued2013
dc.identifier.citationLek, H.H., Poo, D.C.C. (2013). Aspect-based Twitter sentiment classification. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI : 366-373. ScholarBank@NUS Repository. https://doi.org/10.1109/ICTAI.2013.62
dc.identifier.isbn9781479929719
dc.identifier.issn10823409
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/78027
dc.description.abstractDue to the popularity of Twitter, sentiment classification for Twitter has become a hot research topic. Previous studies have approached the problem as a tweet-level classification task where each tweet is classified as positive, negative or neutral. However, getting an overall sentiment might not be useful to organizations which are using twitter for monitoring consumer opinion of their products/services. Instead, it is more useful to determine specifically which aspects of the products/services the users are happy or unhappy about. This paper proposes an aspect-based sentiment classification approach to analyze sentiments for tweets. To the best of our knowledge, we are the first to perform sentiment analysis for Twitter in this manner. We conducted several experiments and show that by incorporating results from the aspect-based sentiment classifier, we are able to improve existing tweet-level classifiers. The experimental results also demonstrated that our approach outperforms existing state-of-the-art approaches. © 2013 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICTAI.2013.62
dc.sourceScopus
dc.subjectAspect-based Sentiment Analysis
dc.subjectOpinion Mining
dc.subjectSentiment Analysis
dc.subjectTwitter Sentiment Analysis
dc.typeConference Paper
dc.contributor.departmentINFORMATION SYSTEMS
dc.description.doi10.1109/ICTAI.2013.62
dc.description.sourcetitleProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
dc.description.page366-373
dc.description.codenPCTIF
dc.identifier.isiut000482633400036
Appears in Collections:Staff Publications

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