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Multi-task learning with mutual learning for joint sentiment classification and topic detection

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Gui, Lin, Jia, Leng, Zhou, Jiyun, Xu, Ruifeng and He, Yulan (2020) Multi-task learning with mutual learning for joint sentiment classification and topic detection. IEEE Transactions on Knowledge and Data Engineering . doi:10.1109/TKDE.2020.2999489

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Official URL: http://dx.doi.org/10.1109/TKDE.2020.2999489

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Abstract

Recently, advances in neural network approaches have achieved many successes in both sentiment classification and probabilistic topic modelling. On the one hand, latent topics derived from the global context of documents could be helpful in capturing more accurate word semantics and hence could potentially improve the sentiment classification accuracy. On the other hand, the word-level attention vectors obtained during the learning of sentiment classifiers could carry word-level polarity information and can be used to guide the discovery of topics in topic modelling. This paper proposes a multi-task learning framework which jointly learns a sentiment classifier and a topic model by making the word-level latent topic distributions in the topic model to be similar to the word-level attention vectors in the classifier through mutual learning. Experimental results on the Yelp and IMDB datasets verify the superior performance of the proposed framework over strong baselines on both sentiment classification accuracy and topic modelling evaluation results including perplexity and topic coherence measures. The proposed framework also extracts more interpretable topics compared to other conventional topic models and neural topic models.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Neural networks (Computer science), Artificial intelligence, Probabilities, Machine learning, Natural language processing (Computer science), Computational linguistics
Journal or Publication Title: IEEE Transactions on Knowledge and Data Engineering
Publisher: IEEE Computer Society
ISSN: 1041-4347
Official Date: 9 June 2020
Dates:
DateEvent
9 June 2020Available
5 June 2020Accepted
Date of first compliant deposit: 23 July 2020
DOI: 10.1109/TKDE.2020.2999489
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
794196[ERC] Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661
EP/T017112/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
103652Innovate UKhttp://dx.doi.org/10.13039/501100006041
61876053[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61632011[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
JCYJ20180507183527919Shenzhen Municipal Science and Technology Innovation CouncilUNSPECIFIED

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