ABSA Methodology Based on Interval-Enhanced Talking-Heads Attention Network

As an important task of fine-grained sentiment analysis, aspect-based sentiment classification faces many challenges. In order to improve the classification accuracy, this paper focuses on the problems of long-distance semantic feature capture, emotional noise filtering, opinion word and aspect matching. This paper proposes an Interval-enhanced Talking-heads Attention Network (ITAN) for aspect-based sentiment analysis. Firstly, the limited semantic interval enhancement module was introduced to limit the semantic interval according to the aspect relative distance threshold, and a variety of word combination relations were generated in the whole sentence. Then, the gated filtering operation is used to integrate and extract the information enhancement representation of the limited interval. At the same time, the talking-heads attention mechanism module is combined to capture the emotional information from different perspectives to ensure that the opinion words and aspects are effectively matched. Finally, the sentiment classifier is used to integrate the information to generate the sentiment representation. The proposed model achieves 86.47%, 82.59%, and 77.10% sentiment classification accuracy on three public datasets: Restaurant, Laptop, and Twitter, respectively. The experimental results show that the proposed model is effective and can effectively improve the performance of aspect-based sentiment classification tasks.