There is a remarkable growth in the usage of social networks, such as Facebook and Twitter. Users from different cultures and backgrounds post large volumes of textual comments reflecting their opinion in different aspect of life and make them available to everyone. In particular we study the case of Twitter and focus on presidential elections in Egypt 2012. This paper compares between two techniques for Arabic text classification using WEKA application. These techniques are Support Vector Machine (SVM) and Naive Bayesian (NB), we investigate the use of TF-IDF to obtain document vector. The main objective of this paper is to measure the accuracy and time to get the result for each classifier and to determine which classifier is more accurate for Arabic text classification.   Comparison reported in this paper shows that the Naive Bayesian method is the highest accuracy and the lowest error rate.
['Tarek Elghazaly', 'Amal Mahmoud', 'Hesham A. Hefny']
Political Sentiment Analysis Using Twitter Data