# Transformer-Based Graph Convolutional Network for Sentiment Analysis
## 1 Introduction  
With the rapid growth of textual content on the Internet such as social networks and e-commerce websites, the need for contextual processing and mining of the subjective information that text holds is increasing [1]. Sentiment analysis, also called opinion mining, is an automatic technology to extract, process, judge, and summarize opinions, attitudes, and emotions from opininative data. Nowadays, text sentiment analysis has become essential for many fields such as movie recommendation, e-commerce, and public opinion analysis [2]. For example, sentiment analysis aims to obtain the sentiment tendency of the person's opinions towards products, hot events, or any specific topic, which helps human decision-making [3]. Generally, researchers have explored three types of sentiment analysis approaches dictionary-based sentiment methods, machine learning-based sentiment methods, and deep learning-based sentiment methods. Sentiment dictionary-based methods utilize dictionaries to determine the sentiment words in the given text and obtain the sentiment values. Then, using the sentiment calculation rules, the sentiment tendency is calculated [4, 5]. The implementation of this approach is easy and does not require labeling samples. However, the quality of sentiment analysis depends on the sentiment dictionaries, which are insufficient to cover the sentiment words and lack the domain words, leading to the low quality of the sentiment analysis. Later, to address the problem of dictionary dependency, machine learning approaches were proposed; such approaches utilize support vector machine SVM algorithm, naive Bayesian algorithm, graph-based semi-supervised classification algorithms to analyze thetext sentiment [6; 7; 8]. Despite the improvement of sentiment analysis that machine learning made, it strongly relies on corpus quality labeled with polarity. In recent years, deep learning models have attracted the attention of many researchers to address the problem of feature extraction. They propose various deep learning-based methods for sentiment analysis, which achieved promising results compared to machine learning methods in sentiment association and sentiment classification [3; 9; 10; 11]. However, deep learning models face the difficulty of extracting more comprehensive sentimental and emotional features since a large amount of emotional information is not utilized. As a result, more researchers try to integrate emotional information [12] and language knowledge [13] into the models [14; 15; 16]. Despite the great success of these models, they face the problem of extracting more comprehensive text emotional features since such models heavily rely on emotional resources and text information. More recently, graph neural networks [17], or graph representation learning is a new research field that has received much attention form researchers. The entire corpus is represented as a graph in graph-based methods [18]. In graph embeddings, graph convolutional networks have proven to be effective at tasks involving knowledge representation and can retain the global structure information of a graph. However, most of the existing GNNs are built to learn node representations on fixed and homogeneous graphs. When learning representations on a misspecified graph or a heterogeneous graph with multiple types of nodes and edges, the restrictions become increasingly severe. In this work, we present a novel text graph transformer networks to address the GNNs issues. The text graph transformer network contains a new graph structure that can determine the useful connections between not directly connected nodes and learn the soft selection of edge types and complex relations. To summarize, our contributions are as follows:  
1.