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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/476284
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dc.contributor.advisorAzuraliza Abu Bakar, Prof. Dr.
dc.contributor.authorJamilu Awwalu (P72257)
dc.date.accessioned2023-10-06T09:15:48Z-
dc.date.available2023-10-06T09:15:48Z-
dc.date.issued2015-07-12
dc.identifier.otherukmvital:80599
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/476284-
dc.descriptionTwitter, an online micro-blogging and social networking service provides registered users the ability to write in 140 characters anything they wish; hence providing them the opportunity to express their opinions and sentiments on events taking place. Politically sentimental tweets are top trending tweets whenever election is by the corner, users tweet about their favourite candidates or political parties and at times give their reasons for that. In this study, Katz Back-off is used for hybridization of N-gram models after applying Laplace smoothing to Naive Bayesian classifier. This was done as a means of smoothing and addressing the limitation of accuracy in terms of precision and recall of N-gram models caused by the 'zero count problem' in N-gram models. Result from the baseline model shows an increase of 6.05% in average F-Harmonic accuracy in comparison to the N-gram model, and 1.75% increase in comparison to the Semantic-Topic model proposed from a previous study on the same dataset i.e. Obama-McCain dataset.,Master of Information Technology
dc.language.isoeng
dc.publisherUKM, Bangi
dc.relationFaculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat
dc.rightsUKM
dc.subjectComputational linguistics -- Online social networks -- Political aspects
dc.subjectInformation theory -- Online social networks -- Political aspects
dc.subjectUniversiti Kebangsaan Malaysia -- Dissertations
dc.subjectDissertations, Academic -- Malaysia
dc.titleHybridised N-gram model using naive bayes for classification of political sentiments on twitter
dc.typetheses
dc.format.pages79
dc.identifier.callnoP98.A976 2015 3 tesis
dc.identifier.barcode002099 (2016)
Appears in Collections:Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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