## 2 Literature Review
### Binary and Multi-Class Classification  
There are many studies in the literature related to binary text classification. Studies have been carried out in this area focusing on the detection of breast and skin cancer [6] and the classification of news-related tweets [22]. Two-class sentiment classification was performed using the SST-2, MR, CoLA and R8 datasets. The Text-GCN, Bi-LSTM, VGCN, BERT, STGCN + BERT + BiLSTM, VGCN-BERT and IMGCN models were used for classification. On other datasets, with the exception of R8, the IMGCN model achieved higher accuracy results than the other models. However, the highest accuracy value on the R8 dataset was obtained when using the STGCN + BERT + BiLSTM model. A two-layer BiLSTM model was utilized to integrate the local feature information obtained from the BERT model with the global information obtained from the GCN model [23]. However, studies using multi-class datasets are increasing day by day. As the number of classes increases, the decision boundaries that the algorithm must solve will also increase. This makes the process more difficult than a binary classification problem. In one of the studies, two different datasets were used. A set of 21,000 seven-class, balanced tweets was used for training and a set of 19,740 seven-class, balanced tweets was used for testing. In the study, three different experiments were conducted using the random forest model. In the first experiment, the seven class labels were consolidated into two classes: positive and negative. In this case, the accuracy value was 0.81. When consolidated into three classes--positive, neutral and negative--the accuracy value was 0.70. When seven class labels were used, the result was 0.60. The model success decreased with increasing numbers of class labels, making learning more difficult [24]. An experimental study was conducted on a total of 10,000 financial news reports from the Times of India, Money Control, Bloomberg and Financial Express news sources between 2017 and 2020. There were four class effects in total, and the unbalanced dataset was balanced using the SMOTE method. The Random Forest, Logistic Regression, Linear SVC, Multi-layer Perceptron and Decision Tree algorithms were used, together with TF-IDF. The highest accuracy value was obtained when using Random Forest, at 0.93. In the next experiment, DistilBERT was used instead of TF-IDF, and an accuracy value of 0.94 was obtained with Random Forest [25]. The combination of WordNet and deep learning-based models such as BERT is currently a hot research topic. In a study using WordNet and BERT, Text-GCN, Bi-LSTM, VGCN, BERT, VGCN-BERT and the proposed Mutual Graph Convolution Networks (MGCN) model were used on two-class SST-2, MR and CoLA datasets. The Mutual Graph Convolution Networks (MGCN) model introduced the semantic dictionary, which is dependent on WordNet and the BERT model. MGCN uses dependency to address context dependence issues and utilizes WordNet to gather additional semantic information. Experimental studies were carried out, and the highest success rate was obtained when using MGCN, the recommended model, which achieved a Macro F1-score of 92.31% [26]. WordNet can also be used for embedding. WN embeddings have demonstrated a reason able color density towards [CLS]. In this study, the WN2V-BERT model was proposed, and the highest accuracy value of 93.23% was obtained on the two-class dataset [6]. In addition to binary classification, studies have been carried out on multi-class datasets using BERT and BERT-like methods. In a related study, experiments were conducted using the LSTM, Bidirectional LSTM, BERT, BERT uncased, DistilBERT, RoBERTa, XLM-RoBERTa, GPT-2, RoBERTa CustomNet and RoBERTa ConvNet methods using four-class news data. The highest results were obtained with the use of RoBERTa, at 91.91% [27]. Studies have shown that the success rate of classification is affected by the increase in the number of classes [24].