Classifying Political Arabic Articles Using Support Vector Machine with Different Feature Extraction In the recent years, the number of web logs, and the amount of opinionated data on the World Wide Web, have been grown substantially. The ability to determine the political orientation of an article automatically can be beneficial in many areas from academia to security. However, the sentiment classification of web log posts (political web log posts in particular), is apparently more complex than the sentiment classification of conventional text. In this paper, a supervised machine learning with two feature extraction techniques Term Frequency (TF) and Term FrequencyInverse Document Frequency (TFIDF) are used for the classification process. For investigation, SVM with four kernels for supervised machine learning have been employed. Subsequent to testing, the results reveal that the linear with TF achieved the results in accuracy of 91.935% also with TFIDF achieved the 95.161%. The linear kernel was deemed the most suitable for our model.

Comparative Analysis for Arabic Sentiment Classification Sentiment analysis categorizes human opinions, emotions and reactions extracted from text into positive or negative polarity. However, mining sentiments from the Arabic text is challenging due to the scarcity of Arabic datasets for training the context. To address this gap, this study builds an Arabic sentiment dataset sourced from tweets, product reviews, hotel reviews, movie reviews, product attraction, and restaurant reviews from different websites; manually labeled for training the sentiment analysis model. The dataset is then used in a comparative experiment with three machine learning algorithms, which are Support Vector Machine (SVM), Naive Bayes (NB), and Decision Tree (DT) via a classification methodology. The best results for polarity prediction in sentiment analysis models was achieved by SVM with product attraction dataset, with the accuracy of 0.96, precision of 0.99, recall of 0.99, and Fmeasure of 0.98. This is followed by the average performance from NB and DT. It can be concluded that the ML classifiers need the right morphological features to enhance the classification accuracy when dealing with different words that play different roles in the sentence with the same letters.