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User-level twitter sentiment analysis with a hybrid approach

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dc.contributor.authorEr, M.J.
dc.contributor.authorLiu, F.
dc.contributor.authorWang, N.
dc.contributor.authorZhang, Y.
dc.contributor.authorPratama, M.
dc.contributor.conference13th International Symposium on Neural Networks, ISNN 2016 (6 Jul 2016 - 8 Jul 2016 : St. Petersburg, Russia)
dc.contributor.editorCheng, L.
dc.contributor.editorLiu, Q.
dc.contributor.editorRonzhin, A.
dc.date.issued2016
dc.description.abstractWith the objective of extracting useful information from the opinion-rich data on Twitter, both supervised learning-based and unsupervised lexicon-based methods for sentiment analysis on Twitter corpus have been studied in recent years. However, the unique characteristics of tweets such as the lack of labels and frequent usage of emoticons poses challenges to most of the existing learning-based and lexicon-based methods. In addition, studies on Twitter sentiment analysis nowadays mainly focus on domain specific tweets while a larger amount of tweets are about personal feelings and comments on daily life events. In this paper, a hybrid approach of augmented lexicon-based and learning-based method is designed to handle the distinctive characteristics of tweets and perform sentiment analysis on a user level, providing us information of specific Twitter users’ typing habits and their online sentiment fluctuations. Our model is capable of achieving an overall accuracy of 81.9%, largely outperforming current baseline models on tweet sentiment analysis.
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016 / Cheng, L., Liu, Q., Ronzhin, A. (ed./s), vol.9719, pp.426-433
dc.identifier.doi10.1007/978-3-319-40663-3_49
dc.identifier.isbn9783319406626
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/11541.2/29017
dc.language.isoen
dc.publisherSpringer
dc.publisher.placeGermany
dc.relation.fundingNational Natural Science Foundation of P. R. China 51009017
dc.relation.fundingNational Natural Science Foundation of P. R. China 51379002
dc.relation.fundingMinistry of Transport of P.R. China 2012-329-225-060
dc.relation.fundingProgram for Liaoning Excellent Talents in University LJQ2013055
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsCopyright 2016 Springer International Publishing Switzerland
dc.source.urihttps://doi.org/10.1007/978-3-319-40663-3_49
dc.subjectTwitter
dc.subjectsocial media
dc.subjectdate mining
dc.subjectsentiment analysis
dc.titleUser-level twitter sentiment analysis with a hybrid approach
dc.typeConference paper
pubs.publication-statusPublished
ror.mmsid9916606138401831

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