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Embedding refinement framework for targeted aspect-based sentiment analysis
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Liang, Bin, Yin, Rongdi, Du, Jiachen, Gui, Lin, He, Yulan, Yang, Min and Xu, Ruifeng (2023) Embedding refinement framework for targeted aspect-based sentiment analysis. IEEE Transactions on Affective Computing, 14 (1). 279 -293. doi:10.1109/TAFFC.2021.3071388 ISSN 1939-1374.
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WRAP-embedding-refinement-framework-targeted-aspect-based-sentiment-analysis-2021.pdf - Accepted Version - Requires a PDF viewer. Download (14Mb) | Preview |
Official URL: http://doi.org/10.1109/TAFFC.2021.3071388
Abstract
The state-of-the-art approaches to Targeted Aspect-Based Sentiment Analysis (TABSA) are mostly deep learning models based on attention mechanisms. One problem in most previous studies is that embeddings of targets and aspects are either pre-trained from large external corpora or randomly initialized. We argue that affective commonsense knowledge and words indicative of sentiment could be used to learn better target and aspect embeddings. We therefore propose an embedding refinement framework called RAEC (Refining Affective Embedding from Context), in which sentiment concepts extracted from affective commonsense knowledge and word relative location information are incorporated to derive context-affective embeddings. Furthermore, a sparse coefficient vector is exploited in refining the embeddings of targets and aspects separately. In this way, embeddings of targets and aspects can capture the highly relevant affective words. Experimental results on two benchmark datasets show that our framework can be easily integrated with existing embedding-based TABSA models and achieves state-of-the-art results compared to models relying on pre-trained word embeddings or built on other embedding refinement methods.
Item Type: | Journal Article | |||||||||||||||||||||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||||||||||||||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Computational linguistics, Natural language processing (Computer science), Object-oriented programming languages | |||||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | IEEE Transactions on Affective Computing | |||||||||||||||||||||||||||||||||||||||
Publisher: | IEEE | |||||||||||||||||||||||||||||||||||||||
ISSN: | 1939-1374 | |||||||||||||||||||||||||||||||||||||||
Official Date: | 1 January 2023 | |||||||||||||||||||||||||||||||||||||||
Dates: |
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Volume: | 14 | |||||||||||||||||||||||||||||||||||||||
Number: | 1 | |||||||||||||||||||||||||||||||||||||||
Page Range: | 279 -293 | |||||||||||||||||||||||||||||||||||||||
DOI: | 10.1109/TAFFC.2021.3071388 | |||||||||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2021 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 | |||||||||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 3 June 2021 | |||||||||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 4 June 2021 | |||||||||||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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