Sentiment Analysis for Arabic Social Media News Polarity

In recent years, the use of social media has rapidly increased and developed significant influence on its users. In the study of the behavior, reactions, approval, and interactions of social media users, detecting the polarity (positive, negative, neutral) of news posts is of considerable importance. This proposed research aims to collect data from Arabic social media pages, with the posts comprising the main unit in the dataset, and to build a corpus of manually-processed data for training and testing. Applying Natural Language Processing to the data is crucial for the computer to understand and easily manipulate the data. Therefore, Stop-Word removal, Stemming, and Normalization are applied. Several classifiers, such as Support Vector Machine, Na?ve Bayes, K-Nearest Neighbor, Random Frost, and Decision Tree are used to train the dataset, and their accuracy is determined by data testing. These two steps are carried out using the open-source WEKA tool. As a result, each post is categorized into three different classes: positive, negative, and neutral. This research concludes that among the classifiers, SVM reaches the highest level of accuracy with a percentage of 83% for the F1-measure.