Transfer learning for aspect based sentiment analysis

Transfer learning has become a key approach for addressing the challenges of aspect-based sentiment analysis (ABSA). By leveraging pre-trained language models, it enables models to generalize more effectively across domains and languages in a wide range of ABSA tasks. While prior surveys in sentiment analysis and ABSA have offered valuable insights, many are now outdated, focus broadly on general sentiment analysis, or lack a systematic categorization of transfer learning techniques. This survey addresses these gaps through an updated, structured review of recent studies, organized around four research questions examining: the types of transfer learning techniques applied in ABSA, the datasets most frequently used, their impact on model performance and generalizability, and which methods prove most effective. The paper also discusses key challenges associated with transfer learning models and outlines future research directions integrating transfer learning with complementary paradigms such as reinforcement, multitask, and federated learning.