## 1 Introduction  
With the rapid growth of the information era, digital users now devote an average of 150 min a day to social networks. Social media user numbers have continued to grow by more than 4 billion over recent years [1]. Therefore, relevant businesses utilize social media to stay in touch with customers and market their products [2, 3]. Clients also browse other people's reviews to learn about the quality of new services or products that they might be interested in. The massive number of comments generated by social media has caught the interest of corporations, governments, and organizations that are interested in better understanding public opinion on various issues and user behavior for concrete objectives [4, 5, 6]. The purpose of sentiment analysis is to extract subjective information from natural language texts to create structured and digital information for further induction and reasoning. Hence, sentiment analysis is critical to social media data analysis. Deep learning models have particularly achieved remarkable success in various natural language processing (NLP) tasks, including text classification and sentiment analysis [7]. [PERSON] et al. introduced a separate framework for the domain of sentiment analysis with weighting rules based on rhetorical structure theory [8]. The authors parsed the text into a rhetorical structure tree and, then, used two generic lexicons to calculate the sentiment scores of the sentences. Finally, they determined the text polarity by summing the scores of the sentences according to the weighting rules. [PERSON] et al. used a convolutional neural network (CNN) to improve the performance of sentiment analysis [9]. They first applied a sequence-based neural network model to obtain word embeddings. The sentences were then classified into three types, based on the number of targets contained in the sentences (non-target, single-target, or multi-target sentences), where each kind of sentence was fed into the model separately. Social media is mainly in the form of short texts, and the actual sentiment of short texts is difficult to predict. The performance of current methods for sentiment analysis relies heavily on the quality of features extracted from the texts [10]. The features of the original sequence data are extended by merging the syntactic information in the sequence (i.e., syntactic analysis trees, such as dependency trees and component analysis trees). To reduce the distance between words, and words from the perspective of graphically structured data, the syntactic analysis trees can encode complex pairwise relationships between them. Thus, the dependency information of sequences is effectively preserved for learning more informative features [11]. The mainstream sentiment analysis methods use semantic analysis based on sequence structure, which lacks syntactic information in the sequences. In addition, previous studies on social media sentiment analysis used traditional neural networks to extract features. These schemes have algorithmic limitations, in that they cannot extract both semantic and syntactic structural information of a sequence. To solve the above problems, we proposed an enhanced long short-term memory neural network (LSTM) using dependency parsing (DP) and a graph convolution network (GCN), referred to as dependency parsing graph long short-term memory (DPG-LSTM). Dependency parsing (DP) refers to a sophisticated NLP task that analyzes the dependency grammar of a sentence, with words and lexemes as input and DTs as output [12]. DP theory assumes that the dominant-subordinate relationship between words is a binary inequality relationship. The modifier is considered dependent when a word affects another word in a phrase; the altered word is called the head, and the grammatical relationship between the two is called the dependency relation. All word dependencies in a sentence are represented as directed edges in a DT.