系统梳理跨语言细粒度情感分析的研究现状与挑战
Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges
- 归纳跨语言ABSA的核心任务与技术路径
- 总结多语言数据集与知识迁移方法
- 适合关注多语言情感分析的研究者参考
细粒度情感分析（ABSA）旨在识别特定方面词、类别或观点的情感倾向。尽管该领域进展显著，但多数研究集中于单语场景。跨语言ABSA致力于将资源丰富语言（如英语）的知识迁移到低资源语言，仍缺乏系统性综述。本文全面回顾了跨语言ABSA的任务体系，包括方面词抽取、情感分类及复合任务；梳理了相关数据集、建模范式与跨语言迁移方法；分析了单语、多语及大模型在该方向的贡献，并指出当前主要挑战与未来研究方向。
原文摘要 · Abstract (English)
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that focuses on understanding opinions at the aspect level, including sentiment towards specific aspect terms, categories, and opinions. While ABSA research has seen significant progress, much of the focus has been on monolingual settings. Cross-lingual ABSA, which aims to transfer knowledge from resource-rich languages (such as English) to low-resource languages, remains an under-explored area, with no systematic review of the field. This paper aims to fill that gap by providing a comprehensive survey of cross-lingual ABSA. We summarize key ABSA tasks, including aspect term extraction, aspect sentiment classification, and compound tasks involving multiple sentiment elements. Additionally, we review the datasets, modelling paradigms, and cross-lingual transfer methods used to solve these tasks. We also examine how existing work in monolingual and multilingual ABSA, as well as ABSA with LLMs, contributes to the development of cross-lingual ABSA. Finally, we highlight the main challenges and suggest directions for future research to advance cross-lingual ABSA systems.
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