INTEGRATING TEXT AND EMOTICONS FOR DETECTING EXTREMIST AFFILIATIONS ON TWITTER USING DEEP LEARNING

Abstract

The main motivation behind this research paper is to address the issue of identifying extremist affiliations on social media platforms. With the rise of social media, people have been given the power to express their opinions and emotions on a global scale, which has led to the emergence of a new form of communication. Unfortunately, some individuals and organizations have been using these platforms to spread hate and propaganda, and even recruit individuals to join their extremist causes. This has created a serious threat to national and global security. Sentiment analysis, specifically opinion mining, has emerged as an important tool for identifying and tracking extremist activities on social media. The proposed deep learning model that utilizes Distil BERT algorithm aims to improve the accuracy of classification by combining text and emoticons for sentiment analysis. The model captures sentiment expressed in both text and emoticons, highlighting the significance of including emoticons in sentiment analysis. This study has the potential to contribute significantly to the field of sentiment analysis and social media monitoring, ultimately aiding in the fight against extremism. The implications of this research can be applied to sentiment analysis in social media and extended to other social media platforms that use emojis to express opinions and emotions. By identifying tweets that support or relate to extremist affiliations, the proposed model can help authorities monitor such activities on social media platforms and take appropriate actions.