Aspect-level Sentiment Analysis based on Prompt Templates and External Knowledge

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task aimed at identifying and evaluating sentiment expressions in text or speech while considering the relationships between multiple aspects of sentiment. Recent research trends favor the adoption of end-to-end frameworks to address ABSA tasks in a unified manner. However, these frameworks can be fine-tuned for downstream tasks without requiring any task-specific adaptations. Specifically, they fail to effectively leverage task-specific knowledge. To address this issue, this chapter proposes an aspect-level sentiment analysis approach that integrates prompt knowledge to enhance the model's perception of downstream tasks and strengthen the semantic interaction between aspect words and context. Firstly, the concept and types of prompt learning are elaborated. Then, the designed model network structure is introduced, which consists of three components: prompt text construction layer, semantic encoding layer, and sentiment label word mapping layer. Subsequently, the design methods and implementation processes of each module are described. Finally, the effectiveness of the proposed approach is validated through comparative experiments on publicly available datasets.