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Title: Sentiment detection of short text via probabilistic topic modeling
Author(s): Wang, Philips Fu Lee 
Xie, Haoran 
Author(s): Wu, Z.
Rao, Y.
Li, X.
Li, J.
Issue Date: 2015
Publisher: Springer
Related Publication(s): Database Systems for Advanced Applications (DASFAA 2015 International Workshops) Revised Selected Papers
Start page: 76
End page: 85
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.
DOI: 10.1007/978-3-319-22324-7_7
CIHE Affiliated Publication: Yes
Appears in Collections:CIS Publication

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