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DC FieldValueLanguage
dc.contributorDepartment of Computingen_US
dc.contributor.advisorChan, Keith C. C. (COMP)-
dc.creatorLi, Bing-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/7951-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleA big data approach to opinion analysis in social mediaen_US
dcterms.abstractSome recent studies have suggested that public opinions expressed in social media may be correlated with various social issues. To find out what actually can be discovered in social media data, we need data mining. Data mining approaches that can handle massive amounts of data have recently been referred to as big data approaches. In this thesis, we propose a big data approach to handling opinion analysis. By means of a literature review on works related to social media opinion mining, we found that the message content or the texts in social media are ambiguous and unstructured and are often in the form of short sentences. In other words, extracting clear and accurate opinions is difficult. To do so, in this study we propose an Ontology-based adapted social media data collection system called the OACM system, as well as a fuzzy big data algorithm called FMM. Through several sets of comparable experiments, the proposed OACM system and FMM have shown their effectiveness. The OACM can optimize system resource scheduling efficiently, and can effectively speed up the collection of large amounts of data in a relatively short time from multiple social media sources. Meanwhile, the FMM can identify opinions expressed in social media data more accurately when compared with other data mining algorithms, and can reduce computing complexity as well as processing time significantly.en_US
dcterms.extentxvii, 204 pages : illustrations ; 30 cmen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2015en_US
dcterms.educationalLevelAll Doctorateen_US
dcterms.educationalLevelPh.D.en_US
dcterms.LCSHData mining.en_US
dcterms.LCSHSocial media.en_US
dcterms.LCSHUser-generated content.en_US
dcterms.LCSHPublic opinion -- Data processing.en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsopen accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/7951