Subjective Text Mining for Arabic Social Media

Nourah F. Bin Hathlian and Alaaeldin M. Hafez
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Nourah F. Bin Hathlian: College of Arts and Sciences, Nairiyah University of Hafer Albatin, Alkhbar, Saudi Arabia
Alaaeldin M. Hafez: College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia

International Journal on Semantic Web and Information Systems (IJSWIS), 2017, vol. 13, issue 2, 1-13

Abstract: The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

Date: 2017
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