Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges

TL;DR

This survey reviews the current state of cross-lingual aspect-based sentiment analysis, highlighting tasks, methods, datasets, challenges, and future research directions in transferring sentiment understanding across languages.

Contribution

It provides the first comprehensive overview of cross-lingual ABSA, covering tasks, approaches, datasets, and challenges, and discusses how monolingual and multilingual ABSA research informs this field.

Findings

Summarizes key cross-lingual ABSA tasks and methods.

Identifies main challenges in transfer learning for ABSA.

Suggests future research directions for improving cross-lingual systems.

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.