Semantic emotion-topic model based social emotion mining

Xue, R; Huang, S; Luo, X; Jiang, D; Da Xu, RY

Semantic emotion-topic model based social emotion mining

Xue, R Huang, S Luo, X Jiang, D Da Xu, RY

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Publication Type:: Journal Article
Citation:: Journal of Web Engineering, 2018, 17 (1-2), pp. 73 - 92
Issue Date:: 2018-03-01

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Field	Value	Language
dc.contributor.author	Xue, R	en_US
dc.contributor.author	Huang, S	en_US
dc.contributor.author	Luo, X	en_US
dc.contributor.author	Jiang, D	en_US
dc.contributor.author	Da Xu, RY	en_US
dc.date.issued	2018-03-01	en_US
dc.identifier.citation	Journal of Web Engineering, 2018, 17 (1-2), pp. 73 - 92	en_US
dc.identifier.issn	1540-9589	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134388
dc.description.abstract	© River Publishers. With the booming of social media users, more and more short texts with emotion labels appear, which contain users' rich emotions and opinions about social events or enterprise products. Social emotion mining on social media corpus can help government or enterprise make their decisions. Emotion mining models involve statistical-based and graph-based approaches. Among them, the former approaches are more popular, e.g. Latent Dirichlet Allocation (LDA)-based Emotion Topic Model. However, they are suffering from low retrieval performance, such as the bad accuracy and the poor interpretability, due to them only considering the bag-of-words or the emotion labels in social media corpus. In this paper, we propose a LDA-based Semantic Emotion-Topic Model (SETM) combining emotion labels and inter-word relations to enhance the retrieval performance of social emotion mining result. The performance influence of four factors on SETM are considered, i.e., association relations, computing time, topic number and semantic interpretability. Experimental results show that the accuracy of our proposed model is 0.750, compared with 0.606, 0.663 and 0.680 of Emotion Topic Model (ETM), Multi-label Supervised Topic Model (MSTM) and Sentiment Latent Topic Model (SLTM) respectively. Besides, the computing time of our model is reduced by 87.81% through limiting word frequency, and its accuracy is 0.703, compared with 0.501, 0.648 and 0.642 of the above baseline methods. Thus, the proposed model has broad prospects in social emotion mining area.	en_US
dc.relation.ispartof	Journal of Web Engineering	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Semantic emotion-topic model based social emotion mining	en_US
dc.type	Journal Article
utslib.citation.volume	1-2	en_US
utslib.citation.volume	17	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
pubs.issue	1-2	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

© River Publishers. With the booming of social media users, more and more short texts with emotion labels appear, which contain users' rich emotions and opinions about social events or enterprise products. Social emotion mining on social media corpus can help government or enterprise make their decisions. Emotion mining models involve statistical-based and graph-based approaches. Among them, the former approaches are more popular, e.g. Latent Dirichlet Allocation (LDA)-based Emotion Topic Model. However, they are suffering from low retrieval performance, such as the bad accuracy and the poor interpretability, due to them only considering the bag-of-words or the emotion labels in social media corpus. In this paper, we propose a LDA-based Semantic Emotion-Topic Model (SETM) combining emotion labels and inter-word relations to enhance the retrieval performance of social emotion mining result. The performance influence of four factors on SETM are considered, i.e., association relations, computing time, topic number and semantic interpretability. Experimental results show that the accuracy of our proposed model is 0.750, compared with 0.606, 0.663 and 0.680 of Emotion Topic Model (ETM), Multi-label Supervised Topic Model (MSTM) and Sentiment Latent Topic Model (SLTM) respectively. Besides, the computing time of our model is reduced by 87.81% through limiting word frequency, and its accuracy is 0.703, compared with 0.501, 0.648 and 0.642 of the above baseline methods. Thus, the proposed model has broad prospects in social emotion mining area.

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http://hdl.handle.net/10453/134388