AI-powered sentiment analysis for classifying harmful content on social media: A case study with ChatGPT Integration

Social media platforms have become essential for communication but have also created spaces where harmful content, including cyberbullying, racism, and other abusive behaviors, thrives. This study employs AI-driven sentiment analysis to classify social media posts into three categories: Abusive, Neutral, and Harmless. A dataset of Twitter posts sourced from Kaggle was preprocessed through steps like noise removal, tokenization, and normalization to ensure readiness for analysis. The Sentiment Analysis Model (ChatGPT Integration) was utilized for classification, leveraging its advanced contextual capabilities to effectively analyze linguistic patterns. The model's performance, with an accuracy of 96%, sensitivity of 90%, and precision of 88%, was validated through a confusion matrix analysis, demonstrating its reliability in identifying harmful content. The findings highlight the model's potential as a scalable solution for mitigating online abuse. Future work will focus on addressing challenges such as class imbalance, integrating multilingual datasets, and implementing real-time monitoring to enhance its usability and impact.