Aspect-based sentiment analysis with component focusing multi-head co-attention networks

User-generated content based on customer opinions and experience has become a rich source of valuable information for enterprises. The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of specific targets from user-generated content. This study proposes a component focusing multi-head co-attention network model which contains three modules: extended context, component focusing, and multi-headed co-attention, designed to improve upon problems encountered in the past. The extended context module improves the ability of bidirectional encoder representations from transformers to handle aspect-based sentiment analysis tasks, and the component focusing module improves the weighting of adjectives and adverbs, to alleviate the problem of average pooling, which treats every word as an equally important term. The multi-head co-attention network is applied to learn the important words in a multi-word target before acquiring the context representation and performs the attention mechanism on the sequence data. The performance of the proposed model is evaluated in extensive experiments on publicly available datasets. The results show that the performance of the proposed model is better than that of the recent state-of-the-art models.