SBTM : topic modeling over short texts

Jianhui PANG, Xiangsheng LI, Haoran XIE, Yanghui RAO*

*Corresponding author for this work

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

17 Citations (Scopus)


With the rapid development of social media services such as Twitter, Sina Weibo and so forth, short texts are becoming more and more prevalent. However, inferring topics from short texts is always full of challenges for many content analysis tasks because of the sparsity of word co-occurrence patterns in short texts. In this paper, we propose a classification model named sentimental biterm topic model (SBTM), which is applied to sentiment classification over short texts. To alleviate the problem of sparsity in short texts, the similarity between words and documents are firstly estimated by singular value decomposition. Then, the most similar words are added to each short document in the corpus. Extensive evaluations on sentiment detection of short text validate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - DASFAA 2016 International Workshops, BDMS, BDQM, MoI, and SeCoP, Proceedings
EditorsJinho KIM, Hong GAO, Yasushi SAKURAI
Place of PublicationSwitzerland
PublisherSpringer-Verlag GmbH and Co. KG
Number of pages14
ISBN (Print)9783319320540
Publication statusPublished - 2016
Externally publishedYes
Event21st International Conference on Database Systems for Advanced Applications - University of Texas at Dallas, Dallas, United States
Duration: 16 Apr 201619 Apr 2016

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


Conference21st International Conference on Database Systems for Advanced Applications
Abbreviated titleDASFAA 2016
Country/TerritoryUnited States
Internet address


  • Biterm topic model
  • Sentiment detection
  • Short text classification
  • Topic-based similarity


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