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dc.contributor.authorDuşçu, Mihail
dc.contributor.authorDanış, Dilek Günneç
dc.date.accessioned2021-02-18T19:42:23Z
dc.date.available2021-02-18T19:42:23Z
dc.date.issued2020-11
dc.identifier.issn0306-4573en_US
dc.identifier.urihttp://hdl.handle.net/10679/7336
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S0306457320308414
dc.description.abstractPolarity classification is one of the most fundamental problems in sentiment analysis. In this paper, we propose a novel method, Sound Cosine Similaritye Matching, for polarity classification of Twitter messages which incorporates features based on audio data rather than on grammar or other text properties, i.e., eliminates the dependency on external dictionaries. It is useful especially for correctly identifying misspelled or shortened words that are frequently encountered in text from online social media. Method performance is evaluated in two levels: i) capture rate of the misspelled and shortened words, ii) classification performance of the feature set. Our results show that classification accuracy is improved, compared to two other models in the literature, when the proposed features are used.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofInformation Processing and Management
dc.rightsrestrictedAccess
dc.titlePolarity classification of twitter messages using audio processingen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-0749-2584 & YÖK ID 121183) Günneç, Dilek
dc.identifier.volume57en_US
dc.identifier.issue6en_US
dc.identifier.wosWOS:000582206800060
dc.identifier.doi10.1016/j.ipm.2020.102346en_US
dc.subject.keywordsAudio processingen_US
dc.subject.keywordsMachine learningen_US
dc.subject.keywordsSentiment analysisen_US
dc.subject.keywordsText normalizationen_US
dc.subject.keywordsTwitteren_US
dc.identifier.scopusSCOPUS:2-s2.0-85087902248
dc.contributor.authorMale1
dc.contributor.authorFemale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff and PhD Student


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