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dc.contributor.authorBravo-Marquez, Felipeen_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.coverage.spatialOmaha, Nebraska, USAen_NZ
dc.date.accessioned2016-12-01T23:19:33Z
dc.date.available2016en_NZ
dc.date.available2016-12-01T23:19:33Z
dc.date.issued2016en_NZ
dc.identifier.citationBravo-Marquez, F., Frank, E., & Pfahringer, B. (2016). From opinion lexicons to sentiment classification of tweets and vice versa: a transfer learning approach. In Proceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, Nebraska, USA, 13-16 October, 2016.(pp. 145–152). Los Alamitos, CA, USA: IEEE Computer Society. http://doi.org/10.1109/WI.2016.29en
dc.identifier.isbn978-1-5090-4470-2en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/10782
dc.description.abstractMessage-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we propose a simple model for transferring sentiment labels from words to tweets and vice versa by representing both tweets and words using feature vectors residing in the same feature space. Tweets are represented by standard NLP features such as unigrams and part-of-speech tags. Words are represented by averaging the vectors of the tweets in which they occur. We evaluate our approach in two transfer learning problems: 1) training a tweet-level polarity classifier from a polarity lexicon, and 2) inducing a polarity lexicon from a collection of polarity-annotated tweets. Our results show that the proposed approach can successfully classify words and tweets after transfer.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEE Computer Societyen_NZ
dc.rightsThis is an author’s accepted version of an article published in the Proceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence. © 2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.sourceWI'16en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectMachine learning
dc.titleFrom opinion lexicons to sentiment classification of tweets and vice versa: a transfer learning approachen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1109/WI.2016.29en_NZ
dc.relation.isPartOfProceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligenceen_NZ
pubs.begin-page145
pubs.elements-id143521
pubs.end-page152
pubs.finish-date2016-10-16en_NZ
pubs.place-of-publicationLos Alamitos, CA, USA
pubs.start-date2016-10-13en_NZ


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