Aspect-based sentiment analysis for fish diseases using a feature interaction model based on adversarial strategy

Aspect-based sentiment analysis has achieved many results in recent years, but most of the research focuses on goods, services, and topics. The research in the medical field is very limited. Considering the problem that the fish disease descriptions contain large numbers of emotional tendencies but the existing data are insufficient in this field, a dataset named Diseases of Fish Animals (DFA) is constructed. In this paper, we propose a feature interaction model based on an adversarial strategy to learn the relationship between aspects and texts by improving the attention mechanism. Aiming at the problem that the fusion effect is reduced due to the difference in feature distribution space during the interaction process, an adversarial strategy is designed to align text and aspect features. Experiments are carried out on the DFA dataset, which involves 7 aspects and 3 sentiment polarity and contains more than 4700 samples. The experimental results show that the accuracy and F1 of the model are 87.07% and 87.19% respectively. Although the model proposed in this paper achieves the best results on DFA, there is still a certain gap for precision medicine diagnosis and further efforts are needed.