# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 2 Related Works
### Aspect Sentiment Classification  
Aspect sentiment classification is a multi-classification task and has been studied more extensively in relation to aspect term extraction. The research on aspect sentiment classification is divided into traditional machine learning-based methods and deep learning methods. The most successful of the traditional machine learning approaches is a feature-based support vector machine (SVM), which engineers features through experts and uses external resources, such as parsers and sentiment dictionaries. Current research on aspect sentiment classification is mainly based on deep learning techniques. Commonly used deep neural networks are recurrent neural networks (RNNs), convolutional neural networks (CNNs), etc. Neural network models can learn continuous text representation from data without any feature engineering to obtain rich text information. [PERSON] et al. [13] proposed the TD-LSTM model to model the correlation between target words and their context for target sentiment classification. Meanwhile, in order to better utilize the target information and capture the relationship between the target word and its context, the TC-LSTM model was proposed, which makes each word cascade with the target word on the basis of the TD-LSTM model and improves the accuracy of the target sentiment classification. Aspect sentiment is determined by the sentiment words in the text. [PERSON] et al. [14] proposed the ATAE-LSTM model, which enables the model to focus on different parts of the sentence when different aspects are involved. [PERSON] et al. [15] proposed the IAN model, which models the target and the context separately and learns them interactively, both focusing on different parts of the sentence and supervising the modeling of the target. To further focus on the degree of influence of different parts of the sentence on the target, [PERSON] et al. [16] proposed the RAM model, which uses multiple attention mechanisms to synthesize important features of complex sentence structures. [PERSON] et al. [17] proposed the AOA model, similar to the IAN model, which can adequately capture the interactions between aspects and contexts and focus on the focused parts of sentences, but cannot handle complex emotional expressions. In aspect sentiment classification tasks, coarse-grained attention mechanisms are prone to information loss. To address this situation, [PERSON] et al. [18] propose the multi-grained attention network model (MGAN) to implement word-level interactions between aspects and contexts. The model utilizes both fine-grained and coarse-grained attention mechanisms to form a multi-grained attention network structure and implements aspect alignment loss to bring additional useful information for aspect sentiment classification, which further improves the performance of the model. The application of attentional mechanisms in aspect sentiment classification tasks may incorrectly identify syntactically irrelevant contextual words as cues for determining aspect sentiment. To solve this problem, [PERSON] et al. [19] proposed the ASGCN model, which uses graph convolutional networks, syntactic information, and remote word dependencies to predict aspect sentiment. The above model does not make full use of contextual semantic information for word embedding based on a static word vector model. After the pre-trained models came out, most researchers started to use pre-trained models, such as BERT, Roberta, etc., for word embedding to obtain more informative word vectors. [PERSON] et al. [20] proposed an attention encoder network (AEN) for modeling and semantic interaction of target words and contexts to address the problem that RNNs model contexts and target words that are difficult to parallelize. [PERSON] et al. [21] argued that fine-tuning the BERT model using a domain-specific corpus and then fine-tuning it in a supervised manner for downstream tasks would improve the performance of the model. [PERSON] et al.