# Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks
## 2 Review of Literature
Therefore, a new SDRN (Synchronous Double-Channel Recurrent Network) model is proposed, which consists of an entity extraction unit, a relationship detection unit, and a synchronization unit, enabling the simultaneous extraction of entities and their relationships. [PERSON] et al. (2019) stated that it is unreasonable to divide the AOTE task into multiple sub-tasks and extract them through multiple channels and proposed a unified grid-labeling task to solve the AOTE task in an End-to-End manner. In ASPE, the problems of small data and insufficient training are compensated by jointly extracting aspect words and determining the sentiment classification task corresponding to aspect words, as well as by training on other data and transferring some parameters to the current network ([PERSON] et al., 2019). With the enormous prospects of social media cosmetic electronic word of mouth (eWOM), it is imperative to examine the influence of cosmetic eWOM on social media and for cosmetic marketers to understand the antecedents that result in cosmetic consumers making a purchase ([PERSON] et al., 2019). Previous research shows that electronic word-of-mouth spread through social media has a strong influence on customers' purchase decisions ([PERSON] et al., 2019). In recent years, the business community has delved into aspect words and their corresponding opinion words in comments and made judgments on sentiment polarity in one step (i.e., ABSA triplet judgment). Since [PERSON] et al. (2019) proposed this task, ASTE has received a lot of attention from scholars. In words of triplet extraction, previous work divided it into several parts, namely AOTE and AFOE. ASTE integrates these two tasks, with [PERSON] et al. (2019) discovering a strong correlation between the three elements of a triplet and designing a sequence labeling joint model that can capture the relationship between the three elements to extract triplets. [PERSON] et al. (2019) transformed the triplet extraction task into two Machine Reading Comprehension (MRC) problems. Firstly, the task of triplet extraction was decomposed into tasks of AE, AOE, and SC; then, left MRC and right MRC were used to process AE, AOE, and SC separately. Faced with the various ABSA sub-tasks, [PERSON] et al. (2019) converted ABSA into a unified generative task. Based on the unified formula, the model solved all ABSA sub-tasks using the End-to-End framework of the pre-trained sequence-to-sequence model BERT and achieved good experimental results on ASTE. [PERSON] et al. (2019) dealt with the triplet extraction task as a term relationship classification task and defined ten categories of term relations. They used a multichannel network to extract features such as vocabulary, grammar, syntax, and position. Experimental results on a large dataset proved the effectiveness of this model. In these works, the task of extracting aspect words, opinion words, and the sentiment expressed by opinion words in a cascading way has a wider application scenario, and cascading methods can also reduce losses and have higher accuracy. Inspired by [PERSON] et al. (2019), we still define 10 word relationships to facilitate the extraction of triplets and process DREN. And the following research hypotheses were made:  
**Research Hypothesis 1:**: _DREN can obtain the polarity (triplet) of aspect and sentiment in one step without giving the aspect._
**Research Hypothesis 2:**: _SWDA can solve the possible discontinuity problem between aspect words and opinion words._
**Research Hypothesis 3:**: _DREN can achieve higher F1 values than GTS-BiLSTM and GTS-BERT._