A survey on aspect-based sentiment analysis: Tasks, methods, and challenges

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introdu...

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Main Authors: ZHANG, Wenxuan, LI, Xin, DENG, Yang, BING, Lidong, LAM, Wai
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/9084
https://ink.library.smu.edu.sg/context/sis_research/article/10087/viewcontent/09996141.pdf
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spelling sg-smu-ink.sis_research-100872024-08-01T15:16:12Z A survey on aspect-based sentiment analysis: Tasks, methods, and challenges ZHANG, Wenxuan LI, Xin DENG, Yang BING, Lidong LAM, Wai As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA. 2023-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9084 info:doi/10.1109/TKDE.2022.3230975 https://ink.library.smu.edu.sg/context/sis_research/article/10087/viewcontent/09996141.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Aspect-based sentiment analysis opinion mining pre-trained language models sentiment analysis Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Aspect-based sentiment analysis
opinion mining
pre-trained language models
sentiment analysis
Databases and Information Systems
spellingShingle Aspect-based sentiment analysis
opinion mining
pre-trained language models
sentiment analysis
Databases and Information Systems
ZHANG, Wenxuan
LI, Xin
DENG, Yang
BING, Lidong
LAM, Wai
A survey on aspect-based sentiment analysis: Tasks, methods, and challenges
description As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.
format text
author ZHANG, Wenxuan
LI, Xin
DENG, Yang
BING, Lidong
LAM, Wai
author_facet ZHANG, Wenxuan
LI, Xin
DENG, Yang
BING, Lidong
LAM, Wai
author_sort ZHANG, Wenxuan
title A survey on aspect-based sentiment analysis: Tasks, methods, and challenges
title_short A survey on aspect-based sentiment analysis: Tasks, methods, and challenges
title_full A survey on aspect-based sentiment analysis: Tasks, methods, and challenges
title_fullStr A survey on aspect-based sentiment analysis: Tasks, methods, and challenges
title_full_unstemmed A survey on aspect-based sentiment analysis: Tasks, methods, and challenges
title_sort survey on aspect-based sentiment analysis: tasks, methods, and challenges
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/9084
https://ink.library.smu.edu.sg/context/sis_research/article/10087/viewcontent/09996141.pdf
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