Abstract
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.
Abstract (translated)
基于方面的情感分析（ABSA）是一项精细粒度的情感分析任务，专注于在特定层面理解意见，包括对具体方面术语、类别和观点的情感态度。尽管ABSA研究已经取得了显著进展，但大多数的研究重点仍然放在单语环境上。跨语言ABSA旨在将知识从资源丰富的语言（如英语）转移到低资源语言中，这一领域仍然是一个尚未充分探索的区域，并且没有系统的文献回顾。本文旨在填补这一空白，提供一份全面的跨语言ABSA调查报告。我们总结了关键的ABSA任务，包括方面术语提取、方面情感分类以及涉及多个情感元素的复合任务。此外，我们还审查了解决这些任务所使用的数据集、建模范式和跨语言迁移方法。我们还探讨了单语和多语ABSA以及使用大规模语言模型（LLMs）进行ABSA的相关工作如何推动跨语言ABSA的发展。最后，本文强调主要挑战，并提出未来研究的方向，以促进跨语言ABSA系统的发展。
URL
https://arxiv.org/abs/2508.09516
