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dc.contributor.advisorWiratunga, Nirmalie
dc.contributor.advisorLothian, Robert
dc.contributor.authorMuhammad, Aminu
dc.date.accessioned2016-08-16T08:58:15Z
dc.date.available2016-08-16T08:58:15Z
dc.date.issued2016-05
dc.identifier.citationMUHAMMAD, A.B. 2016. Contextual lexicon-based sentiment analysis for social media. Robert Gordon University, PhD thesis.en
dc.identifier.urihttp://hdl.handle.net/10059/1571
dc.description.abstractSentiment analysis concerns the computational study of opinions expressed in text. Social media domains provide a wealth of opinionated data, thus, creating a greater need for sentiment analysis. Typically, sentiment lexicons that capture term-sentiment association knowledge are commonly used to develop sentiment analysis systems. However, the nature of social media content calls for analysis methods and knowledge sources that are better able to adapt to changing vocabulary. Invariably existing sentiment lexicon knowledge cannot usefully handle social media vocabulary which is typically informal and changeable yet rich in sentiment. This, in turn, has implications on the analyser's ability to effectively capture the context therein and to interpret the sentiment polarity from the lexicons. In this thesis we use SentiWordNet, a popular sentiment-rich lexicon with a substantial vocabulary coverage and explore how to adapt it for social media sentiment analysis. Firstly, the thesis identifies a set of strategies to incorporate the effect of modifiers on sentiment-bearing terms (local context). These modifiers include: contextual valence shifters, non-lexical sentiment modifiers typical in social media and discourse structures. Secondly, the thesis introduces an approach in which a domain-specific lexicon is generated using a distant supervision method and integrated with a general-purpose lexicon, using a weighted strategy, to form a hybrid (domain-adapted) lexicon. This has the dual purpose of enriching term coverage of the general purpose lexicon with non-standard but sentiment-rich terms as well as adjusting sentiment semantics of terms. Here, we identified two term-sentiment association metrics based on Term Frequency and Inverse Document Frequency that are able to outperform the state-of-the-art Point-wise Mutual Information on social media data. As distant supervision may not be readily applicable on some social media domains, we explore the cross-domain transferability of a hybrid lexicon. Thirdly, we introduce an approach for improving distant-supervised sentiment classification with knowledge from local context analysis, domain-adapted (hybrid) and emotion lexicons. Finally, we conduct a comprehensive evaluation of all identified approaches using six sentiment-rich social media datasets.en
dc.description.sponsorshipNigeria. Petroleum Technology Development Fund.en
dc.description.sponsorshipUsmanu Danfodiyo University.en
dc.language.isoenen
dc.publisherRobert Gordon Universityen
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0 ; Copyright: Aminu Bui Muhammaden
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectSentiment analysisen
dc.subjectSentiWordNeten
dc.subjectContextual analysisen
dc.subjectDomain adaptationen
dc.subjectHybrid sentiment lexiconen
dc.subjectDistant supervisionen
dc.subjectEmotion featuresen
dc.titleContextual lexicon-based sentiment analysis for social media.en
dc.typeTheses and dissertationsen
dc.publisher.departmentFaculty of Design and Technology. School of Computing Science and Digital Media.en
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhDen


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https://creativecommons.org/licenses/by-nc-nd/4.0 ; Copyright: Aminu Bui Muhammad
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0 ; Copyright: Aminu Bui Muhammad