Arabic Text Detection Using Rough Set Theory: Designing a Novel Approach
Amjed Abbas Ahmed, Mohammad Kamrul Hasan, Mustafa Musa Jaber, Sumaia Mohammed Al-Ghuribi, Dhafar Hamed, Wasiq Khan, Ahmed Tareq Sadiq, Abir Hussain
Abstract
The linguistics related research and particularly, sentiment analysis using data-driven approaches, has been growing in recent years. However, the large number of users and excessive amount of information available on social media, make it difficult to detect extremism text on these platforms. The literature revealed a plethora of research studies focusing the sentiment analysis primarily, for English texts, however, very limited studies are available concerning the Arabic language which is the 4th mostly spoken language in the world. We first time in this study, propose a text detection mechanism for extremism orientations distinction in Arabic language, to improve the comprehension of subjective phrases. The study introduces a novel method based on Rough Set theory to enhance the accuracy of selected models and recognize text orientation reliably. Experimental outcomes indicate that the proposed method outperforms existing algorithms by contributing towards feature discriminations. Our method achieved 90.853%, 81.707% and 71.951% accuracies for unigram, bigram, and trigram representations, respectively. This study significantly contributes to the limited research in the field of machine learning and linguistics in Arabic language.