Extremism is a growing threat worldwide that presents a significant danger to public safety and national security. Social networks provide extremists with spaces to spread their ideas through commentaries or tweets, often in Asian English. In this paper, we propose an intelligent approach that cleans the text's content, analyzes its sentiment, and extracts its features after converting it to digital data for machine learning treatments. We apply 16 intelligent machine learning classifiers for extremism detection and classification. The proposed artificial intelligence methods for Asian English language data are used to extract the essential features from the text. Our evaluation of the proposed model with an extremism dataset proves its effectiveness compared to the standard classification models based on various performance metrics. The proposed model achieves 93,6% accuracy for extremism detection and 97,0% for extremism classification.
|Journal||ACM Transactions on Asian and Low-Resource Language Information Processing|
|State||Published - 9 May 2023|
Bibliographical notePublisher Copyright:
© 2023 Association for Computing Machinery.
- Machine learning
- Natural Language Processing
- sentiment analysis
- social networks
ASJC Scopus subject areas
- Computer Science (all)