Intensive maximum entropy model for sentiment classification of short text

Yanghui RAO, Jun LI, Xiyun XIANG, Haoran XIE

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

The rapid development of social media services has facilitated the communication of opinions through microblogs/tweets, instantmessages, 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. Copyright © 2015 Springer International Publishing Switzerland.

Original languageEnglish
Title of host publicationDatabase systems for advanced applications: DASFAA 2015 International Workshops, SeCoP, BDMS, and posters, Hanoi, Vietnam, April 20-23, 2015, revised selected papers
EditorsAn LIU, Yoshiharu ISHIKAWA, Tieyun QIAN, Sarana NUTANONG, Muhammad Aamir CHEEMA
Place of PublicationCham
PublisherSpringer
Pages42-51
ISBN (Electronic)9783319223247
ISBN (Print)9783319223230
DOIs
Publication statusPublished - 2015

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Citation

Rao, Y., Li, J., Xiang, X., & Xie, H. (2015). Intensive maximum entropy model for sentiment classification of short text. In A. Liu, Y. Ishikawa, T. Qian, S. Nutanong, & M. A. Cheema (Eds.), Database systems for advanced applications: DASFAA 2015 International Workshops, SeCoP, BDMS, and posters, Hanoi, Vietnam, April 20-23, 2015, revised selected papers (pp. 42-51). Cham: Springer.

Keywords

  • Sentiment classification
  • Short text analysis
  • Intensive maximum entropy model