A Visual Approach to Tracking Emotional Sentiment Dynamics in Social Network Commentaries


  • Ismail Hossain Southern Illinois University
  • Sai Puppala Southern Illinois University
  • Md Jahangir Alam Southern Illinois University
  • Sajedul Talukder Southern Illinois University
  • Zahidur Talukder University of Texas Arlington




The expansion of social media has unlocked a real-time barometer of public opinion. This paper introduces a novel framework to analyze sentiment shifts in social network comment sections, a reflection of the broader public discourse over time. Leveraging a pre-trained uncased RoBERTa model, we predict emotional scores from user comments, mapping these to key sentiment trends such as Approval, Toxicity, Obscenity, Threat, Hate, Offensive, and Neutral. Our methodology employs machine learning techniques to train a dataset that connects emotional scores with these trends, generating trend probability scores. We utilize a bottom-up recursive algorithm to aggregate emotional scores within comment threads, enabling the prediction of trend scores using three distinct aggregation methods. The results demonstrate that our emotional prediction model achieves an AUC of 0.92, and XGBoost stands out with an F1 score exceeding 0.40. Our research elucidates the temporal evolution of online public sentiment, enhancing the understanding of digital social dynamics and offering insights for strategic online interaction, intervention, and content moderation.




How to Cite

Hossain, I., Puppala, S., Alam, M. J., Talukder, S., & Talukder, Z. (2024). A Visual Approach to Tracking Emotional Sentiment Dynamics in Social Network Commentaries. Proceedings of the International AAAI Conference on Web and Social Media, 18(1), 596-609. https://doi.org/10.1609/icwsm.v18i1.31337