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.
Date Issued
2015
Publisher
Springer
Related Publication(s)
Database Systems for Advanced Applications (DASFAA 2015 International Workshops) Revised Selected Papers
Start page
76
End page
85
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.
SFU Affiliated Publication
Yes
Availability at SFU Library
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