Multimodal Aspect-Based Sentiment Analysis: A survey of tasks, methods, challenges and future directions

Abstract

With the development of social media, users increasingly tend to express their sentiments (broadly including sentiment polarities, emotions and sarcasm, etc.) associated with fine-grained aspects (e.g., entities) in multimodal content (mostly encompassing images and texts). Consequently, automated recognition of sentiments within multimodal content over different aspects, namely Multimodal Aspect-Based Sentiment Analysis (MABSA), has recently become an emergent research area. This paper assesses the state-of-the-art methods in MABSA based on a systematic taxonomy over different subtasks of MABSA. It compiles advanced models for each task and offers a concise overview of popular datasets and evaluation standards. Finally, we discuss the limitations of current research and highlight promising future research directions.