Predict Union Election Results Through Polarity and Subjectivity Detection of Multilingual Opinions

This study examines subjectivity and polarity in multilingual WhatsApp messages exchanged during the employees’ union election at the Mehran University of Engineering and Technology (MUET), Khairpur campus. As social-media platforms generate vast volumes of unstructured multilingual text, sentiment analysis an intersection of natural language processing (NLP) and machine learning (ML) offers a rigorous means of extracting collective opinion. We introduce a novel analytical framework for code-switched discourse in Sindhi and Urdu, thereby contributing to both multilingual NLP research and electoral studies. A corpus of WhatsApp messages reflecting anticipated election outcomes was collected, cleaned, and annotated. Five supervised classifiers—feed-forward neural networks, nonlinear support vector machines, random forests, decision trees, and k-nearest neighbours were trained to detect sentiment polarity and subjectivity. Across all models, Candidate 1 received markedly more favourable sentiment, posting an average positive-sentiment score of 61.48 % versus 37.11 % for Candidate 2. Candidate 1 likewise exhibited superior aggregate performance metrics (mean accuracy = 89.92 %, recall = 86.52 %, precision = 85.12 %, F1-score = 85.81 %). These findings demonstrate the feasibility of fine-grained sentiment mining in under-resourced languages and yield actionable insights for campaign strategists. Future research will enhance classification accuracy through advanced deep-learning architectures and will track dynamic sentiment patterns across multiple social-media platforms throughout the electoral cycle.