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Hidden topic–emotion transition model for multi-level social emotion detection
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Tang, Donglei, Zhang, Zhikai, He, Yulan, Lin, Chao and Zhou, Deyu (2019) Hidden topic–emotion transition model for multi-level social emotion detection. Knowledge-Based Systems, 164 . pp. 426-435. doi:10.1016/j.knosys.2018.11.014 ISSN 0950-7051.
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Official URL: http://dx.doi.org/10.1016/j.knosys.2018.11.014
Abstract
With the fast development of online social platforms, social emotion detection, focusing on predicting readers’ emotions evoked by news articles, has been intensively investigated. Considering emotions as latent variables, various probabilistic graphical models have been proposed for emotion detection. However, the bag-of-words assumption prohibits those models from capturing the inter-relations between sentences in a document. Moreover, existing models can only detect emotions at either the document-level or the sentence-level. In this paper, we propose an effective Bayesian model, called hidden Topic–Emotion Transition model, by assuming that words in the same sentence share the same emotion and topic and modeling the emotions and topics in successive sentences as a Markov chain. By doing so, not only the document-level emotion but also the sentence-level emotion can be detected simultaneously. Experimental results on the two public corpora show that the proposed model outperforms state-of-the-art approaches on both document-level and sentence-level emotion detection.
Item Type: | Journal Article | |||||||||||||||||||||
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Subjects: | B Philosophy. Psychology. Religion > BF Psychology H Social Sciences > HM Sociology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Social media, Emotions, Computer science | |||||||||||||||||||||
Journal or Publication Title: | Knowledge-Based Systems | |||||||||||||||||||||
Publisher: | Elsevier | |||||||||||||||||||||
ISSN: | 0950-7051 | |||||||||||||||||||||
Official Date: | 15 January 2019 | |||||||||||||||||||||
Dates: |
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Volume: | 164 | |||||||||||||||||||||
Page Range: | pp. 426-435 | |||||||||||||||||||||
DOI: | 10.1016/j.knosys.2018.11.014 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
Date of first compliant deposit: | 1 December 2018 | |||||||||||||||||||||
Date of first compliant Open Access: | 16 November 2019 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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