Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks

Abstract

The task of aspect-based sentiment analysis (ASBA) is to identify all the sentiment analyses expressed by specific aspect words in the text. How to identify specific objects (i.e., aspect words), describe the modifiers of the specific objects (i.e., opinion words), and judge the sentiment analysis expressed by opinion words (sentimental classification) in one step has become a focus of research in ASBA. ASTE (Aspect Sentiment Triplet Extraction) based on DREN (Deep Relationship Enhancement Networks) has been proposed in this paper. It aims to extract the aspect words and opinion words in the review text in one-step. They can judge the sentiment analysis expressed by the opinion words. Therefore, the study defines ten kinds of word relations; then, the study uses the parts of the speech feature, syntactic feature, relative position feature and tree distance relative feature to enhance the word representation relationship, which enriches the table of information in the relational matrix. Secondly, based on the word representation of BERT and GCN, the structural information of the texts are extracted; then, further extraction of higher-level word semantic information and word relationship information through SWDA (Sliding Window Dilated Attention) occurs, as SWDA can capture the multi-granularity relationship in words. Finally, the experimental results show that the proposed method is effective.

Keywords

• triplet extraction
• Graph Neural Networks
• attention mechanism