Feature selection and weighting for sentiment analysis

dc.contributor.advisorSong, Fei
dc.contributor.authorNicholls, Chris
dc.date.accessioned2020-12-04T15:03:54Z
dc.date.available2020-12-04T15:03:54Z
dc.date.copyright2010
dc.degree.departmentDepartment of Computing and Information Scienceen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.description.abstractSentiment Analysis is a sub-field of natural language processing and involves automatically classifying a piece of text according to the positive or negative opinions expressed in that text. Two main challenges related to sentiment analysis are identifying the best words, or features, on which to base classification decisions and correctly weighting the contribution of each feature to the sentiment expressed in the text. In this thesis we address these two challenges. We propose a new feature selection method, which automatically identifies features from training examples, and compare it with three other feature selection methods which have been shown to work well in previous research. We also propose a method to weight the importance of features based on their part-of-speech categories. Our experimental results show that the feature selection methods along with our part-of-speech feature weighting method can help improve the performance of sentiment analysis.en_US
dc.identifier.urihttps://hdl.handle.net/10214/22408
dc.language.isoen
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectfeature selectionen_US
dc.subjectweightingen_US
dc.subjectsentiment analysisen_US
dc.subjectnatural language processingen_US
dc.subjectpart-of-speech categoriesen_US
dc.titleFeature selection and weighting for sentiment analysisen_US
dc.typeThesisen_US

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