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|Title:||Sentiment detection of short text via probabilistic topic modeling||Author(s):||Wang, Philips Fu Lee
|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||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.
|URI:||https://repository.cihe.edu.hk/jspui/handle/cihe/783||DOI:||10.1007/978-3-319-22324-7_7||CIHE Affiliated Publication:||Yes|
|Appears in Collections:||CIS Publication|
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