Aspect-Based Sentiment Analysis with Cross-Heads Attention

Aspect level affective analysis aims to identify the positive, negative and neutral emotions of aspect words in sentences. The key is to learn the relationship between aspect words and words in sentences. When learning the relationship between words, the existing convolution gated network uses the time convolution method, and its local time window can not describe the relationship between any words. At the same time, the attention of the existing temporal attention model is independent of each other when analyzing the relationship between words. In order to analyze the complex relationship between aspect words and other words in sentences, an emotion analysis model based on cross attention and convolution gated network is proposed in this paper. Firstly, for a given word vector feature, this paper designs a cross attention module. The module adds crossed linear mapping to the matching scores of query vector and keyword vector in multiple attention, so as to integrate the matching scores in multiple attention, which is used to describe the context word relationship of more complex aspect words. Secondly, this paper uses the gated convolution network to encode the local word relationship, and designs the word position coding module to provide the position coding characteristics of words, so as to analyze the effect of position coding on the analysis of word relationship. Finally, for the above encoded word features, this paper uses time pooling to obtain sentence description, and uses full connection classifier to predict emotion classification markers. The experimental analysis on Rest14 and Laptop14 data sets shows that this method can effectively estimate the score relationship between aspect words and other words. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.