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2016 | OriginalPaper | Chapter

SBTM: Topic Modeling over Short Texts

Authors : Jianhui Pang, Xiangsheng Li, Haoran Xie, Yanghui Rao

Published in: Database Systems for Advanced Applications

Publisher: Springer International Publishing

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Abstract

With the rapid development of social media services such as Twitter, Sina Weibo and so forth, short texts are becoming more and more prevalent. However, inferring topics from short texts is always full of challenges for many content analysis tasks because of the sparsity of word co-occurrence patterns in short texts. In this paper, we propose a classification model named sentimental biterm topic model (SBTM), which is applied to sentiment classification over short texts. To alleviate the problem of sparsity in short texts, the similarity between words and documents are firstly estimated by singular value decomposition. Then, the most similar words are added to each short document in the corpus. Extensive evaluations on sentiment detection of short text validate the effectiveness of the proposed method.

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Metadata
Title
SBTM: Topic Modeling over Short Texts
Authors
Jianhui Pang
Xiangsheng Li
Haoran Xie
Yanghui Rao
Copyright Year
2016
Book
Database Systems for Advanced Applications

Print ISBN: 978-3-319-32054-0

Electronic ISBN: 978-3-319-32055-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-32055-7_4

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