# Aspect-Level Sentiment Analysis Based on Syntax-Aware and Graph Convolutional Networks
###### Abstract
[12] proposed a new approach using graph attention networks that effectively integrates syntactic information to improve aspect-level sentiment classification. [PERSON] et al. [13] integrated the global and local structural information of sentences. By constructing word-document graphs to capture the global dependencies between words, and simultaneously using syntactic structure analysis to mine potential local structural information in sentences, excellent performance results were obtained on multiple datasets. [PERSON] et al. [14] utilized long- and short-term memory networks to learn the features of sentences, and further enhanced the embedding representation on the dependency tree by graph convolutional networks, achieving excellent performance results on four benchmark datasets. There is great improvement in accuracy. However, there is a lack of utilization of sentiment-related knowledge for specific aspects of sentences and fusion of aspect-specific information. With further research, integrating external sentiment knowledge as auxiliary information into aspect-level sentiment analysis tasks is expected to further enhance the performance of the model. [PERSON] et al. [15] constructed a new enhanced sentence dependency graph by using SenticNet sentiment general knowledge on the dependency graph of sentences. [PERSON] et al. [16] incorporated GCN into the gating mechanism to enhance the GCN ability to node information to be fully aggregated. Meanwhile, contextual sentiment knowledge was incorporated into the graph convolutional network to enhance the model's perception of sentiment features, which further proved the effectiveness of sentiment general knowledge in aspect-level sentiment analysis tasks. Given the limitations of the above models, this paper proposes a novel aspect-level sentiment analysis model to address these issues. Firstly, aspectual features are supplemented to the syntax-aware module and semantic enhancement module, and external sentiment knowledge is integrated into a graph convolutional network to enhance the model's ability to perceive the sentiment information; secondly, the semantic information of the sentence is obtained by using a multi-head self-attention mechanism and Point-wise Convolutional Transformer. The obtained syntactic and semantic information is pooled and spliced to obtain the final feature representation based on specific aspects of the sentence. The main contributions of this paper are as follows:  
1. For the task of aspect-level sentiment analysis, we propose a novel model that integrates both syntactic and semantic aspects of sentence-specific aspects of sentiment tendencies. 2. External affective general knowledge is introduced to enhance the model's ability to perceive affective information, and additional aspect-specific information is added to the model to increase the model's sensitivity to different specific aspects of the sentence. 3. Extensive experiments on three benchmark aspect-level sentiment analysis datasets outperform the benchmark models compared, demonstrating the significant superiority of our proposed ASGCN model in aspect-level sentiment analysis tasks.