PHNN: A Prompt and Hybrid Neural Network-Based Model for Aspect-Based Sentiment Classification

Abstract

Aspect-based sentiment classification (ABSC) is an important task in natural language processing (NLP) that aims to predict the sentiment polarity of different aspects in a sentence. The attention mechanism and pre-trained models are commonly used in ABSC tasks. However, a single pre-trained model typically does not perceive downstream tasks very well, and the attention mechanism usually neglects the syntactic information of sentences. In this paper, we propose a prompt and hybrid neural network (PHNN) model, which utilizes the prompt and a hybrid neural network structure to solve the ABSC task. More precisely, it first uses the prompt to convert an input sentence into cloze-type text and utilizes RoBERTa to deal with the input. Then, it applies the graph convolutional neural network (GCN) combined with the convolutional neural network (CNN) to extract the syntactic features of the sentence while using bi-directional long short-term memory (BiLSTM) to obtain the semantic features of the sentence. Further, it utilizes the multi-head attention (MHA) mechanism to learn attention in the sentence and aspect words. Finally, the sentiment polarity of the aspect words is obtained by using the softmax function. Experiments on three benchmark datasets show that PHNN has the best performance compared with other baselines, validating the efficiency of our model.