Aspect-Based Sentiment Analysis Using Interaction Matrix And Global Attention Neural Network

Aspect-based sentiment analysis aims to identify the sentiment polarity of aspects in a given sentence. Although existing neural network models show promising results, they cannot meet the expectations in the case of a single network structure and limited dataset. When an aspect term composes more than one word, many models use the coarse-grained attention mechanism but lead to the unsatisfactory results. Besides, the relative distance between words in a sentence is always out of consideration. In this paper, we propose a model based on the interaction matrix and global attention mechanism to improve the ability of aspect-based sentiment analysis. First of all, the relative distance features of words in a sentence are initialized to enrich word embedding. Second, classic neural networks are applied to extract the essential features of word embedding in a sentence, such as long short-term memory and convolutional neural network. Third, an interaction matrix and global attention mechanism are combined to calculate weighted scores and measure relationships between aspect terms and context words. Finally, sentiment polarity is represented through a softmax layer. Experimental results on restaurant, laptop and twitter datasets show that the performance of the proposed model is superior to other methods.