Sentiment analysis of extremism in social media from textual information. (May 2020)

• Record Type: Journal Article
• Authors: Asif, Muhammad; Ishtiaq, Atiab; Ahmad, Haseeb; Aljuaid, Hanan; Shah, Jalal
• Is Part Of: Telematics and informatics. Volume 48(2020)
• Publication Date: 2020-05
• Subjects: Extremism -- Multilingual -- Lexicons -- Multinomial Naïve Bayes Linear Support Vector Classifier; Telecommunication -- Periodicals; Computer networks -- Periodicals; Télécommunications -- Périodiques; Réseaux d'ordinateurs -- Périodiques; 384
• DOI: 10.1016/j.tele.2020.101345
• Languages: English
• ISSNs: 0736-5853

Abstract

Highlights: A novel approach for multilingual sentimental analysis. The uniqueness of research is to use languages i.e. Urdu, English, and Roman-Urdu. A novel dictionary on multilingual sentiments. Extreme lexicons that have intensity weights are created and used to label dataset. Abstract: Uncertainty in political, religious, and social issues causes extremism among people that are depicted by their sentiments on social media. Although, English is the most common language used to share views on social media, however, other vicinity based languages are also used by locals. Thus, it is also required to incorporate the views in such languages along with widely used languages for revealing better insights from data. This research focuses on the sentimental analysis of social media multilingual textual data to discover the intensity of the sentiments of extremism. Our study classifies the incorporated textual views into any of four categories, including high extreme, low extreme, moderate, and neutral, based on their level of extremism. Initially, a multilingual lexicon with the intensity weights is created. This lexicon is validated from domain experts and it attains 88% accuracy for validation. Subsequently, Multinomial Naïve Bayes and Linear Support Vector Classifier algorithms are employed for classification purposes. Overall, on the underlying multilingual dataset, Linear Support Vector Classifier out-performs with an accuracy of 82%.