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.
|Title of host publication||Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers|
|Editors||Yoshiharu ISHIKAWA, Sarana NUTANONG, An LIU, Tieyun QIAN, Muhammad Aamir CHEEMA|
|Place of Publication||Switzerland|
|Number of pages||10|
|Publication status||Published - 2015|
|Event||The 20th International Conference on Database Systems for Advanced Applications - Hanoi, Viet Nam|
Duration: 20 Apr 2015 → 23 Apr 2015
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||The 20th International Conference on Database Systems for Advanced Applications|
|Abbreviated title||DASFAA 2015|
|Period||20/04/15 → 23/04/15|
Bibliographical noteThe 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