  Sentiment analysis is a crucial task that aims to understand people's
emotional states and predict emotional categories based on multimodal
information. It consists of several subtasks, such as emotion recognition in
conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal
sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis
presents numerous challenges, including modality alignment, unified
input/output forms, and dataset bias. To address these challenges, we propose a
Task-Specific Prompt method to jointly model subtasks and introduce a
multimodal generative framework called UniSA. Additionally, we organize the
benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation
benchmark, SAEval. We design novel pre-training tasks and training methods to
enable the model to learn generic sentiment knowledge among subtasks to improve
the model's multimodal sentiment perception ability. Our experimental results
show that UniSA performs comparably to the state-of-the-art on all subtasks and
generalizes well to various subtasks in sentiment analysis.
