## 1 Introduction  
The field of sentiment analysis has gained significant attention in NLP due to the increasing popularity of mobile Internet and social media. One of the tasks within the field of sentiment analysis is ABSA, which focuses on identifying the sentiment (positive, negative, or neutral) associated with specific aspects within a sentence. For instance, in the review sentence \"The food in the restaurant is delicious, but the service is poor,\" \"food\" and \"service\" are the two aspects, whereas \"delicious\" and \"poor\" are the corresponding affective words. As a result, the two aspects are associated with positive and negative sentiment polarities, respectively. However, a growing number of users are sharing their views online, but complex sentiment expressions make it especially difficult to capture what the users think. This makes some hard work for the ABSA task. The successful solution of the ABSA task allows the model to automatically identify fine-grained sentiment expressions, leading to excellent service and consumer experiences. The original ABSA approach used feature words as the basic unit of analysis. For example, by analyzing which feature words in a text indicate sentiment and which sentiment polarity (positive, negative, neutral) these feature words correspond to. These features are usually words or phrases and can be obtained by manual definition and automatic extraction [1; 2]. However, feature-based approaches face problems such as incomplete feature extraction and insufficient granularity. With the rise in sentiment lexicon, researchers have started to use sentiment lexicon to support ABSA, for instance, applying words from the sentiment lexicon to texts and associating them with aspects. This approach usually requires consideration of factors such as the polarity and strength of sentiment words and the relationships between words. Over the past few years, the rapid progress in deep learning has made it possible for researchers to leverage neural networks in NLP tasks, including ABSA. Deep learning methods have significantly contributed to the development of ABSA techniques. ABSA methods based on neural networks often employ deep learning models such as convolutional neural networks (CNNs) [3] or recurrent neural networks (RNNs) [4] to capture the complex connections between aspects and emotions present in text data. In ABSA, the syntactic structure can provide information about the association between a particular aspect and its corresponding sentiment expression. As a result, when predicting the sentiment polarity of a specific aspect, it is essential to consider the syntactic dependencies between contextual words and aspectual words [5; 6]. These methods can extract long-term syntactic dependencies employing convolution. Existing work that utilizes graph neural networks for sentiment analysis mostly focuses on syntactic dependency information while disregarding external affective knowledge related to the text. In our research, we demonstrate that incorporating external affective knowledge can improve the expression of a specific aspect. We achieve this by reconstructing the syntactic dependency tree and augmenting it with external affective knowledge from the sentiment lexicon. Specifically, we first use the original method to construct dependency graphs based on syntactic dependency trees. Then, we incorporate external affective knowledge by assigning each word in the sentence a sentiment score from the sentiment lexicon during the graph construction process. As a result, each sentence can be modeled as a dependency graph that captures both the dependency relationships between the context and the specific aspect. The resulting affective-knowledge-enhanced dependency graph is then fed into a graph convolutional neural network-based model. Furthermore, the conventional attention mechanism can introduce a considerable amount of noise because of its widely scattered weight values. As a result, it can severely impact the effectiveness of the model.