# 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. triplet extraction; Graph Neural Networks; attention mechanism +
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aspects or aspects of a thing in a comment. For example, in the statement \"The mobile operating system is very smooth, but the battery performance is not good\", the user assesses both the operating system and the phone's battery, each with different sentiment orientations. Such evaluations of multiple aspects with diverse sentiment orientations are common in e-commerce scenarios and public opinion analyses, making aspect-based sentiment analysis a topic of considerable interest in academia and the business community. In recent years, with the progress in deep learning, aspect-based sentiment analysis has witnessed significant advancement. Some studies have proposed the use of attention mechanisms and related variants to process ABSA tasks. [PERSON] et al. [4] and [PERSON] et al. [5] proposed using attention mechanism networks to determine the sentiment polarity associated with a given aspect term in the text. [PERSON] et al. [6] suggested that there is a mutual relationship between aspect words and their context, introducing an interactive attention mechanism to give weights to the words in the context and aspect text. [PERSON] et al. [7] suggested using collaborative attention mechanism to associate aspect words with their context while concatenating the term, embedding the context of the term with the aspect words, enhancing the interaction between aspect words and their contexts. The advent of models like BERT (Bidirectional Encoder Representations from Transformers) and GCN (Graph Neural Networks) has further fueled the emergence of a large number of models based on BERT and GCN that are widely used in ABSA. BERT was presented in [8], which used mask tasks and predict next sentence task to learn the semantic information connecting words and sentences. So, [PERSON] et al. [9] used BERT to extract more auxiliary information about aspect words from the context of comments, while [PERSON] et al. [10] used the syntactic relationship in sentences to construct a graph, and then they used GCN to determine sentiment polarity expressed by aspect words in context. GCN [11] realized the convolution operation on the topological graph with the help of graph theory, and semantic information was extracted via graph instruction and convolution. The above studies were all based on the given attributive words and the contextual information to judge the sentiment polarity expressed by the attributive words in the text. At present, great progress is being made in ASTE for given aspect words.