## 1 Introduction
The DT of the sentence \"The Wall Street Journal just published an interesting piece on cryptocurrencies.\" is shown in Figure 1. The direction of the arrow points from the dominant word to the subordinate word (the convention when visualizing). Visit the website [[https://universaldependencies.org/docsv1](https://universaldependencies.org/docsv1)]([https://universaldependencies.org/docsv1](https://universaldependencies.org/docsv1)) (accessed on 22 December 2022) to understand more about the meaning of dependencies. A DT is a subgraph of a full graph, much as a tree is a particular instance of a graph (a graph composed of all word relations). An algorithm, such as [PERSON]'s algorithm, may be used to determine the maximum spanning tree as a DT if the possibility of each edge in the complete graph belonging to the syntactic tree is scored [13]. Our study considers the syntactic structural information in a text with the help of dependency trees (DTs). To exploit the use of syntactic information in sequences to expand the text features, LSTMs are adopted to capture the semantic information and GCN for the syntactic information [14]. LSTM is the most suitable neural network for capturing word dependencies in text, while a GCN uses parallelism to extract local relevance from the text-space structure. DPG-LSTM gives a fresh insight into the syntactic structure of  
Figure 1: An example of DT. sentences from a natural language perspective, allowing more interactions between text elements to be captured than LSTM and GCN alone. Furthermore, because the constructed dependency graph is an arbitrary graph, with numerous relationships that conventional GCN approaches cannot directly exploit, we propose an attention mechanism that combines DTs to extract semantic and syntactic information. In general, DPG-LSTM can be seen as the intersection between sentiment analysis techniques and social media. The goal of our work is to take advantage of the efforts of DP and DL for analyzing and extracting user sentiment from social media text. Our research can aid in improving the semantic comprehension of social media text. In addition, the attention mechanism we propose can highlight the syntactic information from the contextual information by dependency parsing. The combination of GCN and attention mechanism can further improve classification accuracy. The contributions of this paper are summarized as follows:  
1. Instead of using traditional semantic analysis methods to classify texts directly, we propose a sentiment analysis method that combines syntactic information. 2. We designed an attention mechanism based on dependency parsing, which extracts semantic and syntactic information from texts. 3. The best results are obtained on multiple datasets compared to the current advanced models. The rest of this paper is organized as follows. Section 2 describes the related work. Section 3 presents the background technical details. Section 4 describes our proposed sentiment analysis method incorporating information about sentence syntactic structure. Section 5 presents datasets and experimental results. Finally, Section 6 concludes the paper and suggests possible future research directions.