A framework for mining on Twitter data

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dc.access.optionRestricted Campus Access Only
dc.contributor.advisorDing, Qin
dc.contributor.authorHuang, Yifan
dc.contributor.departmentComputer Science
dc.date.accessioned2017-01-11T21:12:27Z
dc.date.available2019-02-26T14:23:55Z
dc.date.created2016-12
dc.date.issued2016-12-13
dc.date.submittedDecember 2016
dc.date.updated2017-01-11T14:33:46Z
dc.degree.departmentComputer Science
dc.degree.disciplineMS-Computer Science
dc.degree.grantorEast Carolina University
dc.degree.levelMasters
dc.degree.nameM.S.
dc.description.abstractMotivated by the increasing need of information retrieval from social media, a lexicon-based approach Tweet Sentiment Classifier (TSC) is presented to determine sentiment from tweet along with a systematic software for twitter data statistics analysis and topic extraction. The TSC uses annotated dictionaries of words (SentiWordNet) and has a negation detector. While the LDA topic model uses Gibbs Sampling. The entire system is unsupervised. Without the need of training, it has significant advantage on speed comparing to supervised methods. It is robust to provide consistent satisfying results from different topics of twitter data. The performance of the TSC also outperforms one of the baseline sentiment analysis methods.
dc.embargo.lift2019-01-11
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10342/6026
dc.language.isoen
dc.publisherEast Carolina University
dc.subjectText Mining
dc.subjectSentiment Analysis
dc.subject.lcshData mining
dc.subject.lcshInformation retrieval--Computer programs
dc.subject.lcshSocial media
dc.titleA framework for mining on Twitter data
dc.typeMaster's Thesis
dc.type.materialtext

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