Sentiment detection of short text via probabilistic topic modeling

Zewei WU, Yanghui RAO, Xin LI, Jun LI, Haoran XIE, Fu Lee WANG

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

As an important medium used to describe events, the short text is effective to convey emotions and communicate affective states. In this paper, we proposed a classification method based on probabilistic topic model, which greatly improve the performance of sentimental categorization methods on short text. To solve the problems of sparsity and context-dependency, we extract hidden topics behind the text and associate different words by the same topic. Evaluation on sentiment detection of short text verified the effectiveness of the proposed method. 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
Pages76-85
ISBN (Electronic)9783319223247
ISBN (Print)9783319223230
DOIs
Publication statusPublished - 2015

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Statistical Models

Citation

Wu, Z., Rao, Y., Li, X., Li, J., Xie, H., & Wang, F. L. (2015). Sentiment detection of short text via probabilistic topic modeling. 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. 76-85). Cham: Springer.

Keywords

  • Short text classification
  • Sentiment detection
  • Topic-based similarity