A Novel Interaction Convolutional Network Based on Dependency Trees for Aspect-Level Sentiment Analysis

Abstract

Aspect-based sentiment analysis aims to identity the sentiment polarity of a given aspect-based word in a sentence. Due to the complexity of sentences in the texts, the models based on the graph neural network still have issues in the accurately capturing the relationship between aspect words and viewpoint words in sentences, failing to improve the accuracy of classification. To solve this problem, the paper proposes a novel Aspect-level Sentiment Analysis model based on Interactive convolutional network with the dependency trees, named ASAI-DT in short. In particular, the ASAI-DT model first extracts the aspect words representation from the sentence representation trained by the Bi-GRU model. Meanwhile, the self-attention score of both the sentence and aspect representation are calculated separately by the self-attention mechanism, in order to reduce the attention to the irrelevant information. Afterward, the proposed model constructs the sub-tree of the dependency trees for the word, while the attention weight scores of the aspect representations will be integrated into the sub-tree. Therefore, the acquired comprehensive information about aspect words is processed by the graph convolutional network to maximize the retention of valid information and minimize the interference of noise. Finally, the effective information can be preserved more completely in the integrated information through the interactive network. Through a large number of experiments on various data sets, the proposed ASAI-DT model shows both the effectiveness and the accuracy of aspect sentiment analysis, which outperforms many aspect-based sentiment analysis models.