• Twinkle
• Prof. (Dr.)Vishal Kohli

This study presents a unified AI framework for identifying ideological groupings within large online populations by analyzing behavioral and linguistic signals from social media activities such as posts, likes, shares, and follows. Unlike traditional surveys or keyword-based methods, it scales effectively while capturing nuanced belief systems. The approach leverages tone analysis, semantic embeddings, and unsupervised learning to extract emotional and contextual cues beyond content similarity. By integrating textual tone, engagement patterns, and network affiliations across platforms like LinkedIn and Twitter (X), the system reveals latent clusters such as professional idealists, tech libertarians, and socio-political critics. Real-world validation shows improved segmentation purity and interpretability over existing methods. The model operates with minimal human intervention, enabling scalable belief modeling. This provides value for applications like targeted messaging, campaign personalization, and social listening. Ultimately, it bridges psychological tone with semantic understanding for more faithful ideological segmentation.

"A Unified Framework for Ideological Segmentation Using AI on Social Media Activity ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.c236-c241, August-2025, Available :http://www.jetir.org/papers/JETIR2508233.pdf

"A Unified Framework for Ideological Segmentation Using AI on Social Media Activity ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. ppc236-c241, August-2025, Available at : http://www.jetir.org/papers/JETIR2508233.pdf