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.
The research work described in this article has been substantially supported by “the Fundamental Research Funds for the Central Universities”(Project Number: 46000-31121401).
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Acknowledgements
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).
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Wu, Z., Rao, Y., Li, X., Li, J., Xie, H., Wang, F.L. (2015). Sentiment Detection of Short Text via Probabilistic Topic Modeling. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_7
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