# Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
## 1 Introduction  
With the continuous advancement of Internet technology and the growing number of Internet users, a substantial volume of comment data is being generated through information exchange. These valuable comment data, such as reviews on events, products and people, can be leveraged for extracting useful information through sentiment analysis [1]. However, traditional sentence-level sentiment analysis methods have gradually become inadequate in meeting our requirements, as users often pay attention to multiple aspects of the same entity [2]. As illustrated in Figure 1, the sentiment polarity of the aspect \"environment\" is considered positive, whereas the sentiment polarity of the aspect \"food\" is negative. Consequently, ABSA has emerged as a crucial research area in the field of natural language processing (NLP) [3, 4]. The key to addressing the ABSA task lies in precisely identifying the opinion words that hold a substantial influence in determining the sentiment polarity of each aspect [5]. In early stages, ABSA tasks employed a combination of recurrent neural networks (RNNs) and attention mechanisms [6, 7, 8, 9, 10]. These approaches were used to capture aspect-related semantic information and generate aspect-specific sentence representations. However, these methods suffer from limitations when it comes to handling noise introduced by unrelated words. Additionally, they overlook the crucial syntactic dependency informationwithin sentences, and the attention module might unintentionally emphasize irrelevant words due to syntactic omissions. Recently, models have been developed with the objective of establishing relational connections between aspects and their corresponding opinion words [11; 12; 13; 14; 15]. These methods notably improve the model's performance, while they treat all dependencies equally, disregarding the fact that the impacts of dependencies among distinct sentiment words differ significantly in ABSA tasks. From a linguistic perspective, dependencies between words with different sentiment intensities carry distinct meanings. Words exhibiting strong sentiment orientation can play a significant role in ABSA tasks, while words with ambiguous sentiment tendencies may interfere with the model's judgment. From a syntactic perspective, different dependency types have varying levels of importance in sentiment analysis tasks. [PERSON] et al. [17] argue that dependencies of the \"nsubj\" (nominal subject) type, as well as certain modifier dependencies like \"amod\" (adjective modifier) and \"advmod\" (adverb modifier), are more crucial than other types of dependencies. To address the aforementioned limitations, we introduce the LDEGCN model, which is composed of three crucial modules: the semantic feature extraction module, the dependency-enhanced module and the local context weight (LCW) method. The semantic feature extraction module is designed to extract meaningful and contextually relevant features from the output of the embedding layer. It consists of two layers of multi-head attention to assign attention weights to aspects within the context, thereby facilitating the extraction of semantic features. The dependency-enhanced module is utilized to strengthen the dependency relationships between words within a sentence. It focuses on specific dependency relationships by integrating external affective knowledge and dependency types. Specifically, we first construct a dependency graph based on the dependency tree. To harness external sentiment knowledge and explore various dependency types, we utilize SenticNet to assign intensity scores to sentiment words and construct a specific dependency set, \(S_{set}\). As a result, the enhanced graph can effectively capture the emotional connections and dependency relationships between contexts and aspects. Furthermore, the LCW method [18] is employed on the dependency-enhanced graph to weight the dependencies, effectively solving the problem of long-distance dependencies.