Supervised Intensive Topic Models for Emotion Detection over Short Text

Yanghui Rao; Jianhui Pang; Haoran Xie; An Liu; Tak-Lam Wong; Qing Li; Fu Lee Wang

doi:10.1007/978-3-319-55753-3_26

Supervised Intensive Topic Models for Emotion Detection over Short Text

Yanghui Rao^*
, Jianhui Pang
, Haoran Xie
, An Liu
, Tak-Lam Wong
, Qing Li
, Fu Lee Wang

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

7 Citations (Scopus)

Abstract

With the emergence of social media services, documents that only include a few words are becoming increasingly prevalent. More and more users post short messages to express their feelings and emotions through Twitter, Flickr, YouTube and other apps. However, the sparsity of word co-occurrence patterns in short text brings new challenges to emotion detection tasks. In this paper, we propose two supervised intensive topic models to associate latent topics with emotional labels. The first model constrains topics to relevant emotions, and then generates document-topic probability distributions. The second model establishes association among biterms and emotions by topics, and then estimates word-emotion probabilities. Experiments on short text emotion detection validate the effectiveness of the proposed models.

Original language	English
Title of host publication	Database Systems for Advanced Applications
Editors	Selçuk Candan, Lei Chen, Torben Bach Pedersen, Wen Hua
Publisher	Springer International Publishing
Pages	408-422
ISBN (Electronic)	978-3-319-55753-3
ISBN (Print)	978-3-319-55752-6
DOIs	https://doi.org/10.1007/978-3-319-55753-3_26
Publication status	Published - 2017
Event	The 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017) - Nanlin Hotel, Suzhou, China Duration: 27 Mar 2017 → 30 Mar 2017 http://ada.suda.edu.cn/dasfaa2017/

Publication series

Name	Lecture Notes in Computer Science
Volume	10177
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017)
Abbreviated title	DASFAA 2017
Place	China
City	Suzhou
Period	27/03/17 → 30/03/17
Internet address	http://ada.suda.edu.cn/dasfaa2017/

Research Keywords

Emotion detection
Short text analysis
Topic model

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10.1007/978-3-319-55753-3_26

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Rao, Y., Pang, J., Xie, H., Liu, A., Wong, T.-L., Li, Q., & Wang, F. L. (2017). Supervised Intensive Topic Models for Emotion Detection over Short Text. In S. Candan, L. Chen, T. B. Pedersen, & W. Hua (Eds.), Database Systems for Advanced Applications (pp. 408-422). (Lecture Notes in Computer Science; Vol. 10177). Springer International Publishing . https://doi.org/10.1007/978-3-319-55753-3_26

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title = "Supervised Intensive Topic Models for Emotion Detection over Short Text",

abstract = "With the emergence of social media services, documents that only include a few words are becoming increasingly prevalent. More and more users post short messages to express their feelings and emotions through Twitter, Flickr, YouTube and other apps. However, the sparsity of word co-occurrence patterns in short text brings new challenges to emotion detection tasks. In this paper, we propose two supervised intensive topic models to associate latent topics with emotional labels. The first model constrains topics to relevant emotions, and then generates document-topic probability distributions. The second model establishes association among biterms and emotions by topics, and then estimates word-emotion probabilities. Experiments on short text emotion detection validate the effectiveness of the proposed models.",

keywords = "Emotion detection, Short text analysis, Topic model",

author = "Yanghui Rao and Jianhui Pang and Haoran Xie and An Liu and Tak-Lam Wong and Qing Li and Wang, \{Fu Lee\}",

year = "2017",

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editor = "Sel{\c c}uk Candan and Lei Chen and Pedersen, \{Torben Bach\} and Wen Hua",

booktitle = "Database Systems for Advanced Applications",

address = "Switzerland",

note = "The 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017), DASFAA 2017 ; Conference date: 27-03-2017 Through 30-03-2017",

url = "http://ada.suda.edu.cn/dasfaa2017/",

}

Rao, Y, Pang, J, Xie, H, Liu, A, Wong, T-L, Li, Q & Wang, FL 2017, Supervised Intensive Topic Models for Emotion Detection over Short Text. in S Candan, L Chen, TB Pedersen & W Hua (eds), Database Systems for Advanced Applications. Lecture Notes in Computer Science, vol. 10177, Springer International Publishing , pp. 408-422, The 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017), Suzhou, China, 27/03/17. https://doi.org/10.1007/978-3-319-55753-3_26

Supervised Intensive Topic Models for Emotion Detection over Short Text. / Rao, Yanghui; Pang, Jianhui; Xie, Haoran et al.
Database Systems for Advanced Applications. ed. / Selçuk Candan; Lei Chen; Torben Bach Pedersen; Wen Hua. Springer International Publishing , 2017. p. 408-422 (Lecture Notes in Computer Science; Vol. 10177).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Supervised Intensive Topic Models for Emotion Detection over Short Text

AU - Rao, Yanghui

AU - Pang, Jianhui

AU - Xie, Haoran

AU - Liu, An

AU - Wong, Tak-Lam

AU - Li, Qing

AU - Wang, Fu Lee

PY - 2017

Y1 - 2017

N2 - With the emergence of social media services, documents that only include a few words are becoming increasingly prevalent. More and more users post short messages to express their feelings and emotions through Twitter, Flickr, YouTube and other apps. However, the sparsity of word co-occurrence patterns in short text brings new challenges to emotion detection tasks. In this paper, we propose two supervised intensive topic models to associate latent topics with emotional labels. The first model constrains topics to relevant emotions, and then generates document-topic probability distributions. The second model establishes association among biterms and emotions by topics, and then estimates word-emotion probabilities. Experiments on short text emotion detection validate the effectiveness of the proposed models.

AB - With the emergence of social media services, documents that only include a few words are becoming increasingly prevalent. More and more users post short messages to express their feelings and emotions through Twitter, Flickr, YouTube and other apps. However, the sparsity of word co-occurrence patterns in short text brings new challenges to emotion detection tasks. In this paper, we propose two supervised intensive topic models to associate latent topics with emotional labels. The first model constrains topics to relevant emotions, and then generates document-topic probability distributions. The second model establishes association among biterms and emotions by topics, and then estimates word-emotion probabilities. Experiments on short text emotion detection validate the effectiveness of the proposed models.

KW - Emotion detection

KW - Short text analysis

KW - Topic model

UR - https://www.scopus.com/pages/publications/85032290393

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85032290393&origin=recordpage

U2 - 10.1007/978-3-319-55753-3_26

DO - 10.1007/978-3-319-55753-3_26

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-319-55752-6

T3 - Lecture Notes in Computer Science

SP - 408

EP - 422

BT - Database Systems for Advanced Applications

A2 - Candan, Selçuk

A2 - Chen, Lei

A2 - Pedersen, Torben Bach

A2 - Hua, Wen

PB - Springer International Publishing

T2 - The 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017)

Y2 - 27 March 2017 through 30 March 2017

ER -

Supervised Intensive Topic Models for Emotion Detection over Short Text

Abstract

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Research Keywords

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