Trolling Detection System Using Natural Language Processing (NLP)

Abstract

The rise of social media has led to an increase in online trolling, which negatively impacts users' mental health and disrupts digital communities. Detecting and mitigating trolling behavior is a significant challenge due to the evolving nature of language, sarcasm, and contextual variations. This research explores the application of Natural Language Processing (NLP) in developing an automated trolling detection system. By leveraging sentiment analysis, text classification, and deep learning techniques, NLP-based models can identify trolling content with high accuracy. This paper examines various approaches, challenges, and future prospects in NLP-based trolling detection systems.