Analysis and Classification of Arabic Newspapers’ Facebook Pages using Text Mining Techniques

Said Salloum, Chaker Mhamdi, Mostafa Al-Emran, Khaled Shaalan


Text mining and sentiment analysis have been the focus of research due to the increasing availability of opinion data on social networking websites. Current researches dealing with sentiment analysis aim at investigating people’s behaviors. Social networks are rich sources of people’s posts and comments regarding various topics which offer a good soil for research. Arabic language is the fifth among the most extensively utilized languages on the Internet. However, investigating social media posts and comments in Arabic language seems to be under-researched. Accordingly, this paper analyzes and classifies posts and comments of Arabic newspapers’ Facebook pages. A total number of
24 Arab Gulf newspapers’ Facebook pages were investigated where 62327 posts and 9372 comments in Arabic were studied and analyzed. Different text mining techniques were employed to analyze the extracted data. As far as Arab Gulf region is concerned, findings indicated that the UAE is the country that most frequently shares posts on Facebook, followed by Oman and KSA, respectively. Additionally, the findings reveal that videos are the most attracting post type on Arabic newspapers Facebook pages.

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