Methodologies and their comparison in complex compound aspect-based sentiment analysis: A survey

Faiz Ghifari Haznitrama, Ho-Jin Choi, Chin-Wan Chung
{"title":"Methodologies and their comparison in complex compound aspect-based sentiment analysis: A survey","authors":"Faiz Ghifari Haznitrama,&nbsp;Ho-Jin Choi,&nbsp;Chin-Wan Chung","doi":"10.1016/j.aiopen.2025.02.002","DOIUrl":null,"url":null,"abstract":"<div><div>Sentiment analysis as a part of natural language processing (NLP) has received much attention following the demand to understand people’s opinions. Aspect-based sentiment analysis (ABSA) is a fine-grained task from sentiment analysis that aims to classify the sentiment at the aspect level. Throughout the years, researchers have formulated ABSA into various tasks for different scenarios. Unlike early works, current ABSA tasks utilize many elements to provide more details to produce informative results. However, it is difficult to completely explore the works of ABSA because of the many different tasks, terms, and results. This paper surveyed recent studies on ABSA, specifically on its complex compound tasks. We investigated some key elements, problem formulations, and datasets currently utilized by most ABSA communities. We focused on reviewing the latest methodologies and worked to find the current <em>state-of-the-art</em> methodologies by performing a comparative analysis. From our study, we found that there has been a shift to generative methods in solving the ABSA problem, which signifies the evolving emphasis on holistic, end-to-end approaches. Finally, we identified some open challenges and future directions for ABSA research.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 53-69"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AI Open","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666651025000051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Sentiment analysis as a part of natural language processing (NLP) has received much attention following the demand to understand people’s opinions. Aspect-based sentiment analysis (ABSA) is a fine-grained task from sentiment analysis that aims to classify the sentiment at the aspect level. Throughout the years, researchers have formulated ABSA into various tasks for different scenarios. Unlike early works, current ABSA tasks utilize many elements to provide more details to produce informative results. However, it is difficult to completely explore the works of ABSA because of the many different tasks, terms, and results. This paper surveyed recent studies on ABSA, specifically on its complex compound tasks. We investigated some key elements, problem formulations, and datasets currently utilized by most ABSA communities. We focused on reviewing the latest methodologies and worked to find the current state-of-the-art methodologies by performing a comparative analysis. From our study, we found that there has been a shift to generative methods in solving the ABSA problem, which signifies the evolving emphasis on holistic, end-to-end approaches. Finally, we identified some open challenges and future directions for ABSA research.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
45.00
自引率
0.00%
发文量
0
期刊最新文献
Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection Optimal RoPE extension via Bayesian Optimization for training-free length generalization ChatLLM network: More brains, more intelligence Methodologies and their comparison in complex compound aspect-based sentiment analysis: A survey Publisher's Note
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1