MAN: Mutual Attention Neural Networks Model for Aspect-Level Sentiment Classification in SIoT

Abstract

Text sentiment classification is of critical importance to improve the autonomous decision making and communication ability among object peers in the Social Internet of Things (SIoT). To classify sentiment polarity on a fine-grained level, aspect-level sentiment classification has become a promising direction in recent years. However, the existing solutions typically ignore the mutual information between sentences and their respective aspect terms while generally performing sentiment classification by using the simple attention mechanism. Thus, the relevant results seem to be unpromising. We aim to develop a novel neural-network-based model, by relying on the natural language processing model for rich feature extraction, called mutual attention neural networks (MANs), to conduct the aspect-level sentiment classification tasks in this article. Compared with the previous work, our proposed MAN model takes advantage of the bidirectional long short-term memory (Bi-LSTM) networks to obtain semantic dependence of sentences and their respective aspect terms, while learning the sentiment polarities of aspect terms in sentences by proposing the mutual attention mechanism. To evaluate the performance of MAN, we conduct our experiments on three real-world data sets, i.e., LAPTOP, REST, and TWITTER. The experimental results show that our proposed MAN model has significant performance improvements when compared to several existing models, in terms of aspect-level sentiment classification.