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dc.contributor.authorBravo-Marquez, Felipeen_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.date.accessioned2018-11-22T20:29:27Z
dc.date.available2018en_NZ
dc.date.available2018-11-22T20:29:27Z
dc.date.issued2018en_NZ
dc.identifier.citationBravo-Marquez, F., Frank, E., & Pfahringer, B. (2018). Transferring sentiment knowledge between words and tweets. Web Intelligence and Agent Systems: An International Journal, 16(4), 203–220.en
dc.identifier.issn1570-1263en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12209
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 study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIOS Pressen_NZ
dc.rightsThis is the author's accepted version. The final publication is available at IOS Press through http://dx.doi.org/10.3233/WEB-180389”
dc.subjectcomputer scienceen_NZ
dc.subjectsentiment classificationen_NZ
dc.subjectpolarity lexicon expansionen_NZ
dc.subjectTwitteren_NZ
dc.subjecttransfer learningen_NZ
dc.subjectMachine learning
dc.titleTransferring sentiment knowledge between words and tweetsen_NZ
dc.typeJournal Article
dc.relation.isPartOfWeb Intelligence and Agent Systems: an international journalen_NZ
pubs.begin-page203
pubs.elements-id221999
pubs.end-page220
pubs.issue4
pubs.publication-statusAccepteden_NZ
pubs.publisher-urlhttps://www.semanticscholar.org/paper/Transferring-Sentiment-Knowledge-between-Words-and-Bravo-Marquez-Frank/8173411609b20d2ff2380e53573069e8aaac1b95en_NZ
pubs.volume16


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