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dc.contributor.authorBravo-Marquez, Felipeen_NZ
dc.contributor.authorMendoza, Marceloen_NZ
dc.contributor.authorPoblete, Barbaraen_NZ
dc.date.accessioned2016-11-28T19:32:53Z
dc.date.available2013-01-01en_NZ
dc.date.available2016-11-28T19:32:53Z
dc.date.issued2013-01-01en_NZ
dc.identifier.citationBravo-Marquez, F., Mendoza, M., & Poblete, B. (2013). Combining strengths, emotions and polarities for boosting Twitter sentiment analysis. In Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM 2013 - Held in Conjunction with SIGKDD 2013. http://doi.org/10.1145/2502069.2502071en
dc.identifier.isbn9781450323321en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/10763
dc.description.abstractTwitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express sentiments about specific topics or entities with different strengths and intensities, where these sentiments are strongly related to their personal feelings and emotions. A number of methods and lexical resources have been proposed to analyze sentiment from natural language texts, addressing different opinion dimensions. In this article, we propose an approach for boosting Twitter sentiment classification using different sentiment dimensions as meta-level features. We combine aspects such as opinion strength, emotion and polarity indicators, generated by existing sentiment analysis methods and resources. Our research shows that the combination of sentiment dimensions provides significant improvement in Twitter sentiment classification tasks such as polarity and subjectivity.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.urihttp://www.cs.waikato.ac.nz/~fjb11/publications/wisdom2013.pdf
dc.rightsThis is an author’s accepted version of an article published in proceedings of WISDOM'13, August 11 Chicago, U.S.A. © 2013 ACM.
dc.titleCombining strengths, emotions and polarities for boosting Twitter sentiment analysisen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1145/2502069.2502071en_NZ
dc.relation.isPartOfProceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM 2013 - Held in Conjunction with SIGKDD 2013en_NZ
pubs.elements-id143474


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