SBTM : topic modeling over short texts

Jianhui PANG; Xiangsheng LI; Haoran XIE; Yanghui RAO

doi:10.1007/978-3-319-32055-7_4

SBTM : topic modeling over short texts

Jianhui PANG, Xiangsheng LI, Haoran XIE, Yanghui RAO^*

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

18 Citations (Scopus)

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.

Original language	English
Title of host publication	Database Systems for Advanced Applications - DASFAA 2016 International Workshops, BDMS, BDQM, MoI, and SeCoP, Proceedings
Editors	Jinho KIM, Hong GAO, Yasushi SAKURAI
Place of Publication	Switzerland
Publisher	Springer-Verlag GmbH and Co. KG
Pages	43-56
Number of pages	14
ISBN (Print)	9783319320540
DOIs	https://doi.org/10.1007/978-3-319-32055-7_4
Publication status	Published - 2016
Externally published	Yes
Event	21st International Conference on Database Systems for Advanced Applications - University of Texas at Dallas, Dallas, United States Duration: 16 Apr 2016 → 19 Apr 2016 https://theory.utdallas.edu/DASFAA2016/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Volume	9645
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Database Systems for Advanced Applications
Abbreviated title	DASFAA 2016
Country/Territory	United States
City	Dallas
Period	16/04/16 → 19/04/16
Internet address	https://theory.utdallas.edu/DASFAA2016/

Keywords

Biterm topic model
Sentiment detection
Short text classification
Topic-based similarity

Access to Document

10.1007/978-3-319-32055-7_4

Cite this

PANG, J., LI, X., XIE, H., & RAO, Y. (2016). SBTM : topic modeling over short texts. In J. KIM, H. GAO, & Y. SAKURAI (Eds.), Database Systems for Advanced Applications - DASFAA 2016 International Workshops, BDMS, BDQM, MoI, and SeCoP, Proceedings (pp. 43-56). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9645). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-319-32055-7_4

PANG, Jianhui ; LI, Xiangsheng ; XIE, Haoran et al. / SBTM : topic modeling over short texts. Database Systems for Advanced Applications - DASFAA 2016 International Workshops, BDMS, BDQM, MoI, and SeCoP, Proceedings. editor / Jinho KIM ; Hong GAO ; Yasushi SAKURAI. Switzerland : Springer-Verlag GmbH and Co. KG, 2016. pp. 43-56 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "Biterm topic model, Sentiment detection, Short text classification, Topic-based similarity",

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booktitle = "Database Systems for Advanced Applications - DASFAA 2016 International Workshops, BDMS, BDQM, MoI, and SeCoP, Proceedings",

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PANG, J, LI, X, XIE, H & RAO, Y 2016, SBTM : topic modeling over short texts. in J KIM, H GAO & Y SAKURAI (eds), Database Systems for Advanced Applications - DASFAA 2016 International Workshops, BDMS, BDQM, MoI, and SeCoP, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9645, Springer-Verlag GmbH and Co. KG, Switzerland, pp. 43-56, 21st International Conference on Database Systems for Advanced Applications, Dallas, United States, 16/04/16. https://doi.org/10.1007/978-3-319-32055-7_4

SBTM : topic modeling over short texts. / PANG, Jianhui; LI, Xiangsheng; XIE, Haoran et al.
Database Systems for Advanced Applications - DASFAA 2016 International Workshops, BDMS, BDQM, MoI, and SeCoP, Proceedings. ed. / Jinho KIM; Hong GAO; Yasushi SAKURAI. Switzerland: Springer-Verlag GmbH and Co. KG, 2016. p. 43-56 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9645).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - SBTM : topic modeling over short texts

AU - PANG, Jianhui

AU - LI, Xiangsheng

AU - XIE, Haoran

AU - RAO, Yanghui

PY - 2016

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N2 - 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.

AB - 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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SN - 9783319320540

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

PANG J, LI X, XIE H, RAO Y. SBTM : topic modeling over short texts. In KIM J, GAO H, SAKURAI Y, editors, Database Systems for Advanced Applications - DASFAA 2016 International Workshops, BDMS, BDQM, MoI, and SeCoP, Proceedings. Switzerland: Springer-Verlag GmbH and Co. KG. 2016. p. 43-56. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2016 Apr 12. doi: 10.1007/978-3-319-32055-7_4

SBTM : topic modeling over short texts

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