  With the growth of content on social media networks, enterprises and services
providers have become interested in identifying the questions of their
customers. Tracking these questions become very challenging with the growth of
text that grows directly proportional to the increase of Arabic users thus
making it very difficult to be tracked manually. By automatic identifying the
questions seeking answers on the social media networks and defining their
category, we can automatically answer them by finding an existing answer or
even routing them to those responsible for answering those questions in the
customer service. This will result in saving the time and the effort and
enhancing the customer feedback and improving the business. In this paper, we
have implemented a binary classifier to classify Arabic text to either question
seeking answer or not. We have added emotional based features to the state of
the art features. Experimental evaluation has done and showed that these
emotional features have improved the accuracy of the classifier.
