Twitter sentiment analysis, 3-way classification: positive, negative or neutral?

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buir.contributor.authorÇeliktuğ, Mestan Fırat
dc.citation.epage2103en_US
dc.citation.spage2098en_US
dc.contributor.authorÇeliktuğ, Mestan Fıraten_US
dc.contributor.editorSong, Y.
dc.contributor.editorLiu, B.
dc.contributor.editorLee, K.
dc.contributor.editorAbe, N.
dc.contributor.editorPu, C.
dc.contributor.editorQiao, M.
dc.contributor.editorAhmed, N.
dc.contributor.editorKossmann, D.
dc.contributor.editorSaltz, J.
dc.contributor.editorTang, J.
dc.contributor.editorHe, J.
dc.contributor.editorLiu, H.
dc.contributor.editorHu, X.
dc.coverage.spatialSeattle, Washington, USAen_US
dc.date.accessioned2020-01-27T11:03:58Zen_US
dc.date.available2020-01-27T11:03:58Zen_US
dc.date.issued2019en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 10-13 December 2018en_US
dc.descriptionConference Name: 2018 IEEE International Conference on Big Data, Big Data 2018en_US
dc.description.abstractPeople face with the huge amount of information on each day with the advent of big data era. The data amount stored and processed by Facebook, Twitter and other big social networks store (e.g. Instagram) is massive in those days. Online social networks provide great opportunity for propagation of almost any type of information. It's actually much much easier to disseminate an idea/knowledge than previous times. Naturally, this creates information validity and immediate curiosity about mass evaluation problem in general. In this regard, sentimental polarity detection in social media (e.g.Classification of a tweet as negative or positive or neutral) is highly valuable for certain institutions, organizations. The study's main focus is to classify negative, positive and neutral approaches of three (3) annotated twitter datasets. Effect of oversampling, unigram features and other features on overall and class-based accuracy ratios is worked on the datasets. Baseline is reached in dataset-2 experiments. 88% overall accuracy was observed in dataset-1 experiments which outperforms the prior art.Unigram features has shown significant effect on overall accuracy, class-based accuracy balance.en_US
dc.description.sponsorshipBaiduen_US
dc.description.sponsorshipExpedia Groupen_US
dc.description.sponsorshipIEEE Computer Societyen_US
dc.description.sponsorshipSquirrel AI Learningen_US
dc.identifier.doi10.1109/BigData.2018.8621970en_US
dc.identifier.eisbn9781538650356en_US
dc.identifier.isbn9781538650363en_US
dc.identifier.urihttp://hdl.handle.net/11693/52835en_US
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/BigData.2018.8621970en_US
dc.source.titleProceedings of the 2018 IEEE International Conference on Big Data, Big Data 2018en_US
dc.subject3-way sentiment analysisen_US
dc.subjectPolarity detectionen_US
dc.subjectSocial media analysisen_US
dc.subjectTwitter analysisen_US
dc.subjectTwitter sentiment analysisen_US
dc.titleTwitter sentiment analysis, 3-way classification: positive, negative or neutral?en_US
dc.typeConference Paperen_US

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