Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/882
DC FieldValueLanguage
dc.contributor.authorXie, Haoranen_US
dc.contributor.otherRao, Y.-
dc.contributor.otherLi, J.-
dc.contributor.otherXiang, X.-
dc.date.accessioned2021-07-12T13:58:04Z-
dc.date.available2021-07-12T13:58:04Z-
dc.date.issued2015-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/882-
dc.description.abstractThe rapid development of social media services has facilitated the communication of opinions through microblogs/tweets, instant-messages, online news, and so forth. This article concentrates on the mining of emotions evoked by short text materials. Compared to the classical sentiment analysis from long text, sentiment analysis of short text is sometimes more meaningful in social media. We propose an intensive maximum entropy model for sentiment classification, which generates the probability of sentiments conditioned to short text by employing intensive feature functions. Experimental evaluations using real-world data validate the effectiveness of the proposed model on sentiment classification of short text.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.titleIntensive maximum entropy model for sentiment classification of short texten_US
dc.typeconference proceedingsen_US
dc.relation.publicationDatabase Systems for Advanced Applications (DASFAA 2015 International Workshops) Revised Selected Papersen_US
dc.identifier.doi10.1007/978-3-319-22324-7_4-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.isbn9783319223230en_US
dc.description.startpage42en_US
dc.description.endpage51en_US
dc.cihe.affiliatedYes-
item.openairetypeconference proceedings-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.grantfulltextnone-
item.cerifentitytypePublications-
crisitem.author.deptSchool of Computing and Information Sciences-
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