Investigating Public Sentiment on High-Profile Incidents in Pakistan: A Computational Approach for Forensic and Security Insights

Muhammad Majid Hussain; Mishal Muneer; Ali Hussain; Muhammad Faiez; Muhammad Zaman Aslam; Ali Raza

doi:10.54692/ijeci.2025.0901/238

Muhammad Majid Hussain Department of Computer Science & IT, The University of Lahore, Lahore, Pakistan
Mishal Muneer Department of Computer Science & IT, The University of Lahore, Lahore, Pakistan
Ali Hussain Department of Computer Science & IT, The University of Lahore, Lahore, Pakistan
Muhammad Faiez Department of Computer Science & IT, The University of Lahore, Lahore, Pakistan
Muhammad Zaman Aslam Department of Computer Science & IT, The University of Lahore, Lahore, Pakistan
Ali Raza Department of Computer Science, University of Management and Technology Lahore, Pakistan

DOI: https://doi.org/10.54692/ijeci.2025.0901/238

Keywords: Twitter, social media, sentiment analysis, public reaction, Pakistan incidents, Text2Emotion, machine learning, SVM, digital forensics, crisis management, criminology, microblogs, emotion detection, public safety, computational framework

Abstract

Twitter and other social apps have made it easy to stay on top of how people react to breaking news nationwide. A computational framework is applied in this study to research public reaction to the Sialkot lynching, Murree’s snowfall disaster, TLP protests, Johar Town blast and the tragedy in Anarkali market in Pakistan. The results were gathered using Twitter’s latest API and were processed to support informal vocabulary, emojis, emoticons and slang found on microblogs. I labeled sentences with the Text2Emotion library, including the five emotions of Happy, Sad, Angry, Surprise, Fear and Neutral. Six models of machine learning, including Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest and K-Nearest Neighbors (KNN), were taught and tested on incident datasets. Among all methods, SVM achieved the best average results and reached 95.8% accuracy on all datasets. The findings reveal that making sense of microblogs with computational sentiment analysis can strengthen digital forensics, crisis management and criminology related to public safety and widespread communication.