Sentiment detection of short text via probabilistic topic modeling

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

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers
EditorsYoshiharu ISHIKAWA, Sarana NUTANONG, An LIU, Tieyun QIAN, Muhammad Aamir CHEEMA
Place of PublicationSwitzerland
PublisherSpringer, Cham
Number of pages10
ISBN (Electronic)9783319223247
ISBN (Print)9783319223230
Publication statusPublished - 2015
Externally publishedYes
EventThe 20th International Conference on Database Systems for Advanced Applications - Hanoi, Viet Nam
Duration: 20 Apr 201523 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceThe 20th International Conference on Database Systems for Advanced Applications
Abbreviated titleDASFAA 2015
Country/TerritoryViet Nam
Internet address

Bibliographical note

The authors are thankful to the anonymous reviewers for their constructive comments and suggestions on an earlier version of this paper. The research described in this paper has been supported by “the Fundamental Research Funds for the Central Universities” (46000-31610009), and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14).


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


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