Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

Abstract

Aspect-Based Sentiment Analysis (ABSA) is one of the essential research in the field of Natural Language Processing (NLP), of which Aspect Sentiment Quad Prediction (ASQP) is a novel and complete subtask. ASQP aims to accurately recognize the sentiment quad in the target sentence, which includes the aspect term, the aspect category, the corresponding opinion term, and the sentiment polarity of opinion. Nevertheless, existing approaches lack knowledge of the sentence’s syntax, so despite recent innovations in ASQP, it is poor for complex cyber comment processing. Also, most research has focused on processing English text, and ASQP for Chinese text is almost non-existent. Chinese usage is more casual than English, and individual characters contain more information. We propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP studies. In this framework, part of speech (POS) and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text structure. Besides, we design a relation extraction module, which provides a bridge for the overall extraction of the framework. A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric F1. Further experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.