# Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks
###### Abstract
However, a pressing and challenging issue that requires urgent attention is how to determine the polarity of aspect words and sentiment words in one step without specifying pre-existing aspect words. In this scenario, where aspect words are not specified in advance, it is necessary to first identify aspect words and opinion words and then combine aspect words and opinion words to obtain aspect words and their corresponding sentiment polarity (The aspect words, opinion words and sentiment polarity were referred to as triplets in this paper). For example, consider the sentence \"The taste is good but the service could be better\". In this case, the aspect words are \"taste\" and \"the service\", while the opinion words are \"good\" and \"could be better\". Consequently, the triplets derived from the above sentence are (\"taste\", \"good\", \"positive\") and (\"the service\", \"could be better\", \"negative\"). It is worth noting that obtaining all these triplets from a comment sentence in a single step poses a more intricate challenge compared with determining the sentiment polarity of aspect words when the aspects are specified. Therefore, the research questions considered in this study are as follows:  
Research Question 1: What method can be used to obtain the polarity (triplet) of aspect and sentiment in one step without giving the aspect? Research Question 2: How can we solve the problem of possible discontinuity between aspect and opinion in ABBA? Research Question 3: What is the effect of the method derived from research question 1? To solve the problem of extracting triplets from comments, [PERSON] et al. [12] introduces a methodology involving the construction of a term relationship matrix from all the words present in the comments. Triplets are then extracted based on the assessment of these term relationships. Based on [PERSON] et al. [12], this paper introduces DREN designed to evaluate term relationships. To address the above three issues, we proposed DREN. Our contribution is as follows: first, we will transform the problem of extracting triplets into a word relationship classification problem and define ten's word relationships, which obtain the polarity (triplet) of aspect and sentiment in one step. In words with more details, this paper employs BERT to extract term information from the text to identify term relationships comprehensive, solves the problem of nested entity term embedding, and obtains the relationship information between words through the LBAM (Linear Biaffine Attention Mechanism) and GCN. Secondly, since aspect words and opinion words may appear non-sequentially in the sentence, SWDA is employed to effectively capture the relationships between words situated at varying distances from each other. Thirdly, this paper also uses the part-of-speech features, syntactic features, relative position features, and tree distance relative features in the comment to fully utilize the information in the comment text and integrates these features with the text representation. The experimental results on a large number of publicly available ASTE-related datasets demonstrate the effectiveness of the proposed model.