## 2 Literature Review  
Recently, several researchers have attempted to resolve problems associated with existing news text classification methods, such as their low efficiency and low classification accuracy, using machine learning and deep learning techniques. Here, we briefly review the available body of related work on news classification. In this regard, [PERSON] and [PERSON] [5] proposed a new news classification method based on deep learning convolutional neural networks (CNN). The main contribution of this research was the highlighting of how the words in the text were weighted. Instead of using word frequency criteria, [PERSON] and [PERSON] created a graph-based technique that described co-occurrent relations between the text's words and preserved structural information of the text to improve the text classification of English news articles. This method was termed the graph-of-words model, in which a graph represents every text in a dataset, and the weighting process follows the graph creation for every text. The TF-IDF criterion (term-frequency/inverse document frequency) forms the basis for weighting every word in a text if the bag-of-words model expresses the text. The datasets R8 and WebKB were used to categorize a collection of texts based on their classes from an existing set of predefined classes. Preprocessing steps were used to recognize the essential words in the dataset, including tokenization, lower-casing, stemming, noise removal, lemmatization, and stop-words removal. The significance of a word in a text was defined utilizing vertex centrality criteria, such as degree centrality and closeness. The results showed that the method's accuracy was superior as it had the highest accuracy for all the WebKB and R8 datasets. In another study using CNN for news classification, [PERSON] et al. [6] compared a CNN with other machine learning classification techniques, including KNN, Naive Bayes, and SVM. The authors utilized the widely used AGs news classification dataset and the Twenty Newsgroup dataset, using metrics such as accuracy, training time, and prediction time to compare the classification techniques. The authors found that SVM provided more accuracy with moderate training time and quick predictions, while KNN took less time for training. Moreover, if the number of news articles to be tested is large and less time is available for testing, then when applying CNN or SVM techniques use of bag-of-words for preprocessing should be considered. [PERSON] [7] also proposed using a deep learning method, recommending CNN for news text classification to resolve the problem of low classification accuracy and low efficiency related to the existing classification methods used for news texts. The study determined the weight of the news text data by utilizing a viable system model (VSM) vector space model. [PERSON] utilized a hash algorithm for encoding news text data to evaluate news. Moreover, the basic structure of CNN was analyzed, and the convolutional layer of CNN was utilized to determine the change value of the convolutional kernel; news text data were trained and a news text classifier was built using CNN to complete the news text classification. It was found that using CNN to improve the accuracy and speed of news text classification was feasible as the method was shorter and faster. [PERSON] and [PERSON] [8] utilized Naive Bayes for word vectorization in their study of machine learning techniques for news classification. The preprocessing methods used in this study were new tokenization, diacritic removal, stop-word removal, and word stemming, while count vectorization and TF-IDF vectorization were used for feature engineering. The researchers utilized a news classification dataset (json), precision, recall, the F-score, and support as performance measures to compare the Naive Bayes classifier with TF-IDF. The findings of this study suggested that Naive Bayes can successfully categorize news, while TF-IDF was not efficient in terms of the performance measures used in the study.