Polarity classification of twitter messages using audio processing
dc.contributor.author | Duşçu, Mihail | |
dc.contributor.author | Danış, Dilek Günneç | |
dc.date.accessioned | 2021-02-18T19:42:23Z | |
dc.date.available | 2021-02-18T19:42:23Z | |
dc.date.issued | 2020-11 | |
dc.identifier.issn | 0306-4573 | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/7336 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/abs/pii/S0306457320308414 | |
dc.description.abstract | Polarity 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.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Information Processing and Management | |
dc.rights | restrictedAccess | |
dc.title | Polarity classification of twitter messages using audio processing | en_US |
dc.type | Article | en_US |
dc.peerreviewed | yes | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0002-0749-2584 & YÖK ID 121183) Günneç, Dilek | |
dc.identifier.volume | 57 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.wos | WOS:000582206800060 | |
dc.identifier.doi | 10.1016/j.ipm.2020.102346 | en_US |
dc.subject.keywords | Audio processing | en_US |
dc.subject.keywords | Machine learning | en_US |
dc.subject.keywords | Sentiment analysis | en_US |
dc.subject.keywords | Text normalization | en_US |
dc.subject.keywords | en_US | |
dc.identifier.scopus | SCOPUS:2-s2.0-85087902248 | |
dc.contributor.authorMale | 1 | |
dc.contributor.authorFemale | 1 | |
dc.relation.publicationcategory | Article - International Refereed Journal - Institutional Academic Staff and PhD Student |
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