Attribute-level Sentiment Analysis Incorporating Auxiliary Information

Abstract

In order to obtain the sentiment tendency of multiple attributes contained in the data reviews, this paper uses an attribute-level sentiment analysis model RBT-BiAtt-GCN, which incorporates auxiliary information and attention mechanism, to analyze the attribute-level sentiment analysis of e-commerce reviews of citrus. The model incorporates auxiliary information, attention mechanism, and GCN network on the basis of the previous one, and the final macro F1 value obtained can reach 78.21%, which achieves good results in comparison with other models.