Efficient Aspect-Based Sentiment Analysis for Conversational Recommendation Based on a Distilled TinyBERT Model

Aspect-Based Sentiment Analysis (ABSA) has emerged as a pivotal task in fine-grained sentiment analysis, enabling detailed insights into user opinions across various applications. Traditional methods and deep learning models, particularly transformer-based architectures like BERT, have significantly advanced ABSA but often at the cost of high computational demands. This chapter delves into the integration of ABSA within conversational recommendation systems, which require dynamic and responsive sentiment analysis. The proposed methodology leverages a distilled TinyBERT model, balancing high accuracy with computational efficiency. Through a knowledge distillation framework, the chapter demonstrates how a smaller, more efficient model can achieve performance comparable to larger transformer models. The experimental setup, using the INSPIRED dataset, showcases the effectiveness of this approach, highlighting its potential for real-time applications. The chapter concludes with a discussion on future directions, emphasizing the need for dynamic adaptation to new aspects in ever-evolving conversational environments.