Sentiment detection of short text via probabilistic topic modeling

Zewei Wu; Yanghui Rao; Xin Li; Jun Li; Haoran Xie; Fu Leewang

doi:10.1007/978-3-319-22324-7_7

Sentiment detection of short text via probabilistic topic modeling

Zewei Wu, Yanghui Rao, Xin Li, Jun Li, Haoran Xie, Fu Leewang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers
Editors	Yoshiharu Ishikawa, Sarana Nutanong, An Liu, Tieyun Qian, Muhammad Aamir Cheema
Pages	76-85
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-319-22324-7_7
Publication status	Published - 2015
Externally published	Yes
Event	2nd International Workshop on Semantic Computing and Personalization, SeCoP 2015, 2nd International Workshop on Big Data Management and Service, BDMS 2015 held in conjunction with 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015 - Hanoi, Viet Nam Duration: 20 Apr 2015 → 23 Apr 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9052
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Semantic Computing and Personalization, SeCoP 2015, 2nd International Workshop on Big Data Management and Service, BDMS 2015 held in conjunction with 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015
Country/Territory	Viet Nam
City	Hanoi
Period	20/04/15 → 23/04/15

Keywords

Sentiment detection
Short text classification
Topic-based similarity

Access to Document

10.1007/978-3-319-22324-7_7

Cite this

Wu, Z., Rao, Y., Li, X., Li, J., Xie, H., & Leewang, F. (2015). Sentiment detection of short text via probabilistic topic modeling. In Y. Ishikawa, S. Nutanong, A. Liu, T. Qian, & M. A. Cheema (Eds.), Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers (pp. 76-85). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9052). https://doi.org/10.1007/978-3-319-22324-7_7

Wu, Zewei ; Rao, Yanghui ; Li, Xin et al. / Sentiment detection of short text via probabilistic topic modeling. Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers. editor / Yoshiharu Ishikawa ; Sarana Nutanong ; An Liu ; Tieyun Qian ; Muhammad Aamir Cheema. 2015. pp. 76-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Sentiment detection of short text via probabilistic topic modeling",

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.",

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

author = "Zewei Wu and Yanghui Rao and Xin Li and Jun Li and Haoran Xie and Fu Leewang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 2nd International Workshop on Semantic Computing and Personalization, SeCoP 2015, 2nd International Workshop on Big Data Management and Service, BDMS 2015 held in conjunction with 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015 ; Conference date: 20-04-2015 Through 23-04-2015",

year = "2015",

doi = "10.1007/978-3-319-22324-7_7",

language = "English",

isbn = "9783319223230",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "76--85",

editor = "Yoshiharu Ishikawa and Sarana Nutanong and An Liu and Tieyun Qian and Cheema, {Muhammad Aamir}",

booktitle = "Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers",

}

Wu, Z, Rao, Y, Li, X, Li, J, Xie, H & Leewang, F 2015, Sentiment detection of short text via probabilistic topic modeling. in Y Ishikawa, S Nutanong, A Liu, T Qian & MA Cheema (eds), Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9052, pp. 76-85, 2nd International Workshop on Semantic Computing and Personalization, SeCoP 2015, 2nd International Workshop on Big Data Management and Service, BDMS 2015 held in conjunction with 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015, Hanoi, Viet Nam, 20/04/15. https://doi.org/10.1007/978-3-319-22324-7_7

Sentiment detection of short text via probabilistic topic modeling. / Wu, Zewei; Rao, Yanghui; Li, Xin et al.
Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers. ed. / Yoshiharu Ishikawa; Sarana Nutanong; An Liu; Tieyun Qian; Muhammad Aamir Cheema. 2015. p. 76-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9052).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sentiment detection of short text via probabilistic topic modeling

AU - Wu, Zewei

AU - Rao, Yanghui

AU - Li, Xin

AU - Li, Jun

AU - Xie, Haoran

AU - Leewang, Fu

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Sentiment detection

KW - Short text classification

KW - Topic-based similarity

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SN - 9783319223230

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

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BT - Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers

A2 - Ishikawa, Yoshiharu

A2 - Nutanong, Sarana

A2 - Liu, An

A2 - Qian, Tieyun

A2 - Cheema, Muhammad Aamir

T2 - 2nd International Workshop on Semantic Computing and Personalization, SeCoP 2015, 2nd International Workshop on Big Data Management and Service, BDMS 2015 held in conjunction with 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015

Y2 - 20 April 2015 through 23 April 2015

ER -

Wu Z, Rao Y, Li X, Li J, Xie H, Leewang F. Sentiment detection of short text via probabilistic topic modeling. In Ishikawa Y, Nutanong S, Liu A, Qian T, Cheema MA, editors, Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers. 2015. p. 76-85. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-22324-7_7

Sentiment detection of short text via probabilistic topic modeling

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