# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 1 Introduction  
Product reviews contain information about consumers' emotions regarding the product. By performing fine-grained recognition and sentiment analysis on the review text, it facilitates potential consumers to evaluate the product or service to help them make purchase decisions. Sentiment analysis is a fundamental task in natural language processing [1]. Coarse-grained sentiment analysis [2] is targeted at the chapter level or utterance level, which is difficult to meet people's needs and obtain useful sentiment information. Fine-grained sentiment analysis targets aspects of a product or service in the text and analyzes the aspects of sentiment expressed by the user. Aspect refers to the attributes of the product. Aspect-based sentiment analysis (ABSA) [3] is a fine-grained sentiment analysis task that aims to identify aspects of a text and judge their sentiment polarity. An aspect-based sentiment analysis task consists of two subtasks, aspect term extraction (ATE) and aspect sentiment classification (ASC). Aspect-based sentiment analysis can provide a more comprehensive and in-depth analysis than chapter-level or utterance-level sentiment analysis. For example, given the text \"While the ambiance was great, the food and service could have been a lot better.\" Aspect terms are \"ambiance\", \"food\", and \"service\". The aspect-based sentiment is positive, negative, and negative, respectively. The text is \"quality of food needs to be improved\". The aspect term is \"quality of food\" and the aspect sentiment is negative. The following problems exist in the aspect-based sentiment analysis task, which is explored based on the above text. There are some problems in the aspect-based sentiment analysis task and the following questions are explored based on the above text. C1: the low level of interaction between ATE and ASC tasks to fully federate multitasks. Most previous aspect-based sentiment analysis models focus only on the accuracy of aspect sentiment classification while neglecting the study of aspect term extraction and lacking the interaction between tasks. The current aspect-based sentiment analysis model cannot take full advantage of the relationship between multiple tasks to achieve mutual regulation between tasks. For example, in the above text, the aspect term \"quality of food\" is first determined in order to accurately determine the aspect sentiment, and the aspect term can be adjusted according to whether the aspect sentiment is wrong or not. In this paper, we provide a more efficient end-to-end aspect-based sentiment analysis solution relative to the model mentioned in Section 4.2, implementing both aspect term extraction and aspect sentiment classification. The flow of the aspect-based sentiment analysis task is shown in Figure 1.  
C2: the word vector lacks local information features and the term boundaries are blurred in terms of multiple words. Aspect term extraction task can be viewed as a sequence labeling task, using the BIO tag to annotate textual data. When the current word label is determined according to the global sequence features, the connection between the context cannot be effectively combined, and the phenomenon of aspect term splitting and label confusion occurs. For example, in the above text, the aspect term is \"quality of food\" instead of \"quality of\" and \"food\", and the error phenomenon of \"I\" in front of \"B\" may occur. C3: the scope of the local context is difficult to define. For the solution of aspect sentiment classification, [PERSON] et al. [4] proposed a local context focus mechanism to reduce the interference of non-local context on aspect sentiment analysis. Later, [PERSON] et al. [5] applied syntactic dependencies to determine the semantic relative distance and, thus, the local context. However, due to the varying length of the text, setting a fixed threshold of syntactic relative distance will easily introduce other negative words, which will affect the judgment of the aspect emotion.