## 2 Literature Review
In another study on news classification using Naive Bayes, [PERSON] and [PERSON] [9] implemented a single-class text classifier system for online news posts. The sample dataset consisted of close to 75,000 news articles from seven websites with their tags. The authors used Naive Bayes and several other classification algorithms, such as KNN (K-nearest neighbors), SVM (support vector machine), and LR (logistic regression) for classification. They compared diverse classifier outcomes from various news sites on the same dataset. The study's results confirmed that Naive Bayes showed comparatively better results of 93% accuracy, while KNN displayed the least results with 72% accuracy. The study confirmed that Naive Bayes performed better when using various news datasets with sufficient accuracy. Further investigation of other publications of ML classifier research revealed an important work by [PERSON] et al. [10], who proposed an approach to classifying news articles according to specific categories using support vector machine (SVM)-based classifiers. Specifically, in order to prove the usability and efficacy of the established methods, they tested least square (LS)-SVM, twin (TW)-SVM, and LS-TWSVM for news classification. The authors reported that LS-TWSVM outperformed the other variants of SVM. Notably, none of the works considered in this literature review classified news articles based on their geographical locations or country of origin. In a recent study, [PERSON] [11] introduced Newsmap, a semi-supervised, data-driven approach, to classify news articles based on geographical location. He relied on three classification methods: simple keyword matching, geographical information extraction systems, and a semi-supervised ML classifier that he developed. The first approach, namely, simple keyword matching, was used to extract traits from articles related to locations and people associated with the location, such as USA and [PERSON]. These traits were then run through a geographical information extraction system that included a manually compiled dictionary of many countries with their associated people and traits. The author relied on the semi-supervised approach to remove the burden of manually classifying thousands of news articles to train the model. Using this approach, the classifier could identify places, people, and organizations associated with locations. Moreover, the classifier was able to identify the country associated with the article based on continuous scores attached to words presented in the article with minimal need for human involvement. This research demonstrated experimentally that Newsmap produced promising results of 80% for precision and recall. Similar work was also carried out by [PERSON] and [PERSON] [4], who developed a model that classified articles based on their countries of origin. Their work relied on the part of speech tagging the articles, retaining only noun-singular (NN), noun-plural (NNS), proper-noun-plural (NNPS), and proper-noun-singular (NNP) tags. The task was to assign these tags to their corresponding articles to link them to a specific city. The authors used ML classifiers to train and test their model: Naive Bayes, SVM, and RF. The latter outperformed both previous classifiers, though the other two also achieved promising scores. Recently, [PERSON] et al. [12] developed an Arabic pilgrim dataset that included over 10,000 news articles about the Hajj from three Arabic-speaking countries: Saudi Arabia, Jordan, and Egypt. They also proposed a supervised machine learning model that classified Arabic articles based on their country of origin. Their model extracted four textual feature categories, including polarity, linguistics, emotions, and part of speech. When combining these features with the BoW approach, their model achieved an F-score of 85% using a support vector machine. The ML algorithms used in this study were RF, SVM, Naive Bayes, and LR.