新型细粒度情感注释和数据管理指南:一个案例研究

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Expert Systems Pub Date : 2025-02-23 DOI:10.1111/exsy.70022
Wei Dai, Wanqiu Kong, Tao Shang, Jianhong Feng, Jiaji Wu, Tan Qu
{"title":"新型细粒度情感注释和数据管理指南:一个案例研究","authors":"Wei Dai,&nbsp;Wanqiu Kong,&nbsp;Tao Shang,&nbsp;Jianhong Feng,&nbsp;Jiaji Wu,&nbsp;Tan Qu","doi":"10.1111/exsy.70022","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Driven by the rise of the internet, recent years have witnessed the gradual manifestation of commercial values of online reviews. In movie industry, sentiment analysis serves as the foundation for mining user preferences among diverse and multi-layered audiences, providing insight into the market value of movies. As a representative task, aspect-based sentiment analysis (ABSA) aims to analyse and extract fine-grained sentiment elements and their relations in terms of discussed aspects. Relevant studies, particularly in the realm of deep learning research, face challenges due to insufficient annotated data. To alleviate this problem, we propose a guideline for fine-grained sentiment annotations that defines aspect categories, describes the method for annotating aspect sentiment triplets, either simple or complex and designs a scheme to represent hierarchical labels. Based on this, an ABSA dataset tailored for the movie domain is curated by annotating on 1100 Chinese short reviews acquired from Douban. Applicability of both the annotation guideline and curated data is evaluated through inter-annotator consistency and self-consistency checks, and domain adaptation assessment of e-commerce and healthcare cases. Predictive performance of machine learning models on this dataset shed light on possible applications in more fine-grained sentiment analysis in the movie domain, for example, figuring out the aspects from which to stimulate viewership and influence public opinions, thereby providing substantial support for the movie's box office performance. Finally, we extended our fine-grained sentiment annotation guideline to the e-commerce and healthcare. Through empirical experimentation, we demonstrated the universality of these guideline across diverse domains.</p>\n </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Guideline for Novel Fine-Grained Sentiment Annotation and Data Curation: A Case Study\",\"authors\":\"Wei Dai,&nbsp;Wanqiu Kong,&nbsp;Tao Shang,&nbsp;Jianhong Feng,&nbsp;Jiaji Wu,&nbsp;Tan Qu\",\"doi\":\"10.1111/exsy.70022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Driven by the rise of the internet, recent years have witnessed the gradual manifestation of commercial values of online reviews. In movie industry, sentiment analysis serves as the foundation for mining user preferences among diverse and multi-layered audiences, providing insight into the market value of movies. As a representative task, aspect-based sentiment analysis (ABSA) aims to analyse and extract fine-grained sentiment elements and their relations in terms of discussed aspects. Relevant studies, particularly in the realm of deep learning research, face challenges due to insufficient annotated data. To alleviate this problem, we propose a guideline for fine-grained sentiment annotations that defines aspect categories, describes the method for annotating aspect sentiment triplets, either simple or complex and designs a scheme to represent hierarchical labels. Based on this, an ABSA dataset tailored for the movie domain is curated by annotating on 1100 Chinese short reviews acquired from Douban. Applicability of both the annotation guideline and curated data is evaluated through inter-annotator consistency and self-consistency checks, and domain adaptation assessment of e-commerce and healthcare cases. Predictive performance of machine learning models on this dataset shed light on possible applications in more fine-grained sentiment analysis in the movie domain, for example, figuring out the aspects from which to stimulate viewership and influence public opinions, thereby providing substantial support for the movie's box office performance. Finally, we extended our fine-grained sentiment annotation guideline to the e-commerce and healthcare. Through empirical experimentation, we demonstrated the universality of these guideline across diverse domains.</p>\\n </div>\",\"PeriodicalId\":51053,\"journal\":{\"name\":\"Expert Systems\",\"volume\":\"42 4\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Expert Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/exsy.70022\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/exsy.70022","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

近年来,在互联网兴起的推动下,网络评论的商业价值逐渐显现。在电影行业中,情感分析是挖掘多样化、多层次观众的用户偏好的基础,可以洞察电影的市场价值。基于方面的情感分析(ABSA)是一种具有代表性的任务,其目的是根据所讨论的方面来分析和提取细粒度的情感元素及其关系。相关研究,特别是深度学习领域的研究,由于标注数据不足而面临挑战。为了缓解这个问题,我们提出了一个细粒度情感注释的准则,该准则定义了方面类别,描述了注释方面情感三元组(简单或复杂)的方法,并设计了一个表示分层标签的方案。在此基础上,通过对从豆瓣获取的1100条中文短评论进行标注,构建了一个适合电影领域的ABSA数据集。通过注释者之间的一致性和自一致性检查以及电子商务和医疗保健案例的领域适应性评估来评估注释指南和整理数据的适用性。机器学习模型在该数据集上的预测性能揭示了在电影领域更细粒度的情感分析中的可能应用,例如,找出刺激观众和影响公众意见的方面,从而为电影的票房表现提供实质性支持。最后,我们将细粒度情感注释指南扩展到电子商务和医疗保健领域。通过实证实验,我们证明了这些准则在不同领域的普遍性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Guideline for Novel Fine-Grained Sentiment Annotation and Data Curation: A Case Study

Driven by the rise of the internet, recent years have witnessed the gradual manifestation of commercial values of online reviews. In movie industry, sentiment analysis serves as the foundation for mining user preferences among diverse and multi-layered audiences, providing insight into the market value of movies. As a representative task, aspect-based sentiment analysis (ABSA) aims to analyse and extract fine-grained sentiment elements and their relations in terms of discussed aspects. Relevant studies, particularly in the realm of deep learning research, face challenges due to insufficient annotated data. To alleviate this problem, we propose a guideline for fine-grained sentiment annotations that defines aspect categories, describes the method for annotating aspect sentiment triplets, either simple or complex and designs a scheme to represent hierarchical labels. Based on this, an ABSA dataset tailored for the movie domain is curated by annotating on 1100 Chinese short reviews acquired from Douban. Applicability of both the annotation guideline and curated data is evaluated through inter-annotator consistency and self-consistency checks, and domain adaptation assessment of e-commerce and healthcare cases. Predictive performance of machine learning models on this dataset shed light on possible applications in more fine-grained sentiment analysis in the movie domain, for example, figuring out the aspects from which to stimulate viewership and influence public opinions, thereby providing substantial support for the movie's box office performance. Finally, we extended our fine-grained sentiment annotation guideline to the e-commerce and healthcare. Through empirical experimentation, we demonstrated the universality of these guideline across diverse domains.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Expert Systems
Expert Systems 工程技术-计算机:理论方法
CiteScore
7.40
自引率
6.10%
发文量
266
审稿时长
24 months
期刊介绍: Expert Systems: The Journal of Knowledge Engineering publishes papers dealing with all aspects of knowledge engineering, including individual methods and techniques in knowledge acquisition and representation, and their application in the construction of systems – including expert systems – based thereon. Detailed scientific evaluation is an essential part of any paper. As well as traditional application areas, such as Software and Requirements Engineering, Human-Computer Interaction, and Artificial Intelligence, we are aiming at the new and growing markets for these technologies, such as Business, Economy, Market Research, and Medical and Health Care. The shift towards this new focus will be marked by a series of special issues covering hot and emergent topics.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信