A Social Cognitive-Inspired Social Media Sentiment Classification Model
Received: 10 June 2025 | Revised: 17 July 2025, 1 August 2025, and 13 August 2025 | Accepted: 20 August 2025 | Online: 8 December 2025
Corresponding author: Neetha Natesh
Abstract
Social cognition, encompassing the perception, interpretation, and response to social stimuli, plays a vital role in understanding human interactions. Nowadays, with the surge of social media platforms, analyzing social cognition through digital text has become an imperative task. Traditional cognitive architectures like ACT-R and SOAR face challenges in processing large-scale, unstructured data, such as those produced from social media platforms. This study addresses this problem by proposing a fine-tuned BERT-based model for understanding social cognition and emotions. The objective was to enhance social cognitive emotion classification accuracy and contextual understanding of social interactions. The methodology involved fine-tuning BERT with adaptations inspired by cognitive models, enabling the classification of emotions (neutral, positive, and negative) and the analysis of social interaction patterns. The model was evaluated on a Twitter dataset, achieving an accuracy of 83.1%, outperforming existing models such as RoBERTa (80.54%) and standard BERT (79.25%). The results underscore the effectiveness of task-specific fine-tuning in capturing social and emotional cognition. This work’s novelty lies in integrating transformer-based models with cognitive principles, providing a robust, scalable framework for analyzing social cognition.
Keywords:
social cognition, transformer models, BERT, emotion classification, cognitive modeling, social media analysisDownloads
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Copyright (c) 2025 Neetha Natesh, M. V. Vijayakumar
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