## 1 Introduction  
ALSC is an entity-oriented, fine-grained system of sentiment classification [1]. \"Entity-oriented\" refers to the sentiment classification of a specific entity or object, while \"fine-grained\" refers to the more detailed and specific classification of a sentiment, which more accurately captures and expresses the differences and nuances of a sentiment in a text. Specifically, it involves analyzing the polarity of a sentiment for all aspect terms (perhaps including more than one) within a sentence [2]. While traditional sentiment classification tasks usually only classify an entire sentence in terms of its sentiment, in ALSC, all the aspects of a text are sentiment-classified, which in turn provides a more nuanced understanding of the different aspects. Take \"The food is outstanding, but the surrounding in this restaurant is the pits\" from the SemEval-2014 Restaurant dataset as an example. In this sentence, the aspect words are \"food\" and \"surrounding\". The two aspects of the sentence express two different sentiment polarities, with \"food\" being a positive opinion and \"surrounding\" being a negative opinion. As shown in Figure 1, ALSC separately classifies the two aspects of the sentence in terms of their sentiment, labeling them as negative for \"surrounding\" and positive for \"food\". However, in traditional sentiment classification, the entire sentence is labeled as a negative sentiment. Therefore, it is essential to analyze the aspect-level emotional polarity to understand public feedback and improve product services. Numerous studies have shown that establishing the connections between the aspect terms and their corresponding syntactic structures and semantic information is necessary for solving aspect-level sentiment classification tasks [3; 4; 5]. Intuitively, sentence dependencies (syntactic structure) emphasize extracting the modifier and modified relationships among the individual words of a sentence, and understanding the syntactic roles and associations between them can help to integrate dependencies into aspect polarity classification tasks to better capture the sentiment words associated with the aspects. Dependency relations are now broadly adopted by many advanced ALSC methods [6; 7]. It has been shown that GAT helps in ALSC tasks [8; 9], and combining dependencies with GAT can greatly boost the behavior of sentiment classification [10]. Many approaches have represented dependencies as adjacency matrices and have then used graph neural networks (e.g., graph attention networks [11] (GAT) or graph convolutional networks [12] (GCNs)) to encode them. Some methods have utilized the topology of dependencies for modeling [13], as well as distance methods based on dependency parse trees [14], which are effective at capturing the complex relationships between sentiment elements. In addition, semantic information [15] has also been used in sub-tasks, such as feature extraction and sentence classification, to optimize the judgment of sentiment polarity by identifying and capturing the complex relationships and semantic expressions in sentences. In linguistics, semantic information refers to both the literal meaning and the context and context-related information. In recent years, many studies [16; 17; 18] have combined the syntactic structure and semantic information to analyze sentence sentiment, a method which has been able to comprehensively capture the sentiment-related aspects of the sentence and reduce information loss. Meanwhile, attention has also been directed extensively to graph neural networks [19; 20] and ALSC tasks, which can productively focus attention on the most relevant parts of a text, thus enabling a deeper understanding of the sentiment polarity. There are some current approaches [21; 22] that have incorporated knowledge to enhance the behavior of the model and combine it skillfully with the attention or syntax. In conjunction with the ALSC task, we propose an ALSC fusion network based on graph neural networks.