A Comprehensive Review of Arabic and English Sentiment Analysis in BBC and SANAD News

News agencies connect global events to local communities. It plays a pivotal role in influencing public opinion. Thus, the necessity arises to recognize news article’s sentiment. The purpose of this paper is to analyze sentiment for English and Arabic news articles in terms of positivity, negativity, or neutrality. Analyzing the articles of Arabic and English news can be challenging from the perspective of morphology. In this paper, we introduce 4 Machine Learning methods, including Logistic Regression (LR), k Nearest Neighbors (KNN), Random Forests (RF) and Naive Bayes (NB), with the TF-IDF as the feature extraction. The study was validated using 2 data sets (BBC, SANAD Arabic news), and two learning models (Hold out and 10-fold cross-validation). The evaluation was based on; Accuracy (ACC), Precision (PREC), Recall (REC), F1-score (F1), and The Matthews Correlation Coefficient (MCC) where it shows an outstanding performance for ML on a 10-fold strategy. The experiments provided in the paper indicated that the proposed ML models achieved the best results.