## 1 Introduction  
In the last decade, there has been a tremendous growth in the number of digital documents and complex textual data. Text classification is a classically important task in many natural language processing (NLP) applications, such as sentiment analysis, topic labeling, and question answering. Sentiment analysis is particularly and significantly important in the realms of business and sales, as it enables organizations to gain valuable insights and make informed decisions. In the age of information explosion, processing and classifying large volumes of textual data manually is time-consuming and challenging. Moreover, the accuracy of manual text classification can be easily influenced by human factors such as fatigue and insufficient domain knowledge. It is desirable to automate classification methods using machine learning techniques, which are more reliable. Additionally, this can contribute to increase the efficiency of information retrieval and reduce the burden of information overload by effectively locating the required information [1]. See some of the works on text classification as well as some of its numerous real-world applications in [2, 3, 4, 5].  
On the other hand, graphs provide an important data representation that is used in a wide range of real-world problems. Effective graph analysis enables users to gain a deeper understanding of the underlying information in the data and can be applied in various useful applications such as node classification, community detection, link prediction, and more. Graph representation is an efficient and effective method for addressing graph analysis tasks. It compresses graph data into a lower-dimensional space while attempting to preserve the structural information and characteristics of the graph to the maximum extent.  
Graph neural networks (GNNs) represent today central notions and tools in a wide range of machine learning areas since they are able to employ the power of both graphs and neural networks in order to operate on data and perform machine learning tasks such as text classification. In this work, we generally aim to pursue some routes of investigation on text classification by GNNs and, in particular, to offer methods which combine ideas of ensemble learning, GNN structures, language models, and graph partitioning techniques for the task of text classification.  
Now, we briefly detail our methods, results, proposed approaches, and experiments. In this paper, we will examine and conduct a careful investigation of various combinations of different language models as well as ensemble learning methods and several graph neural network structures (GNNs). In particular, we employ an ensemble method combination of graph neural networks (such as GCN, GAT, GIN) with pre-trained models (such as BERT [6] and ParsBERT [7]), and graph partitioning methods for text classification of non-English datasets, in particular datasets in Persian. The process involves first organizing the data in the form of a graph, followed by generating initial node features using multi-lingual BERT or ParsBERT. Then, we utilize graph neural network algorithms such as GCN to perform learning and predictions.  
According to the obtained results, the performance of ParsBERT was better than that of BERT in both scenarios of balanced and unbalanced data, for example, in the case of a two-class dataset in balanced data, when using BERT we reached an accuracy of 81 percent and when using ParsBERT we reached an accuracy of 87 percent. Also, using either BERT or ParsBERT and in both balanced and unbalanced data scenarios, when using graph neural networks such as GCN, the performance of the model increases. For example, using BERT and a GCN, we reached a 91 percent accuracy score, which shows the importance of using graph neural networks. Also, by using the ensemble learning method, the accuracy of the model increased to 93 percent in the case of unbalanced data and a two-class dataset.