Question Identification in Arabic Language Using Emotional Based Features

TL;DR

This paper presents an approach to automatically identify questions in Arabic social media text by using emotional features to improve classification accuracy, aiding customer service and feedback analysis.

Contribution

The study introduces emotional features into question identification for Arabic text, enhancing classifier performance over existing methods.

Findings

Emotional features improve classification accuracy.

Binary classifier effectively distinguishes questions from non-questions.

Enhanced method aids customer service automation.

Abstract

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.