• Nistha Hiteshkumar Chauhan
• Bhavana Hotchandani

The Social Barometer is a modular analytical framework designed to decode user behavior and engagement trends across social media platforms. By integrating sentiment analysis, sarcasm detection, engagement metrics, user profiling, and image clustering, the system offers a comprehensive approach to understanding digital interactions. Leveraging advanced natural language processing (NLP) and machine learning techniques, it extracts insights from archived datasets obtained from platforms such as Instagram, Twitter, and LinkedIn. The framework reveals thematic patterns in sentiment, influence, and visual storytelling, supporting applications in influencer performance evaluation, brand reputation monitoring, and social media analytics. Its scalable design allows for flexibility across use cases, and future developments will focus on expanding data modalities, enhancing real-time processing capabilities, and improving sarcasm detection accuracy. This research contributes a holistic tool for advancing real-time, multimodal social media analysis in academic, commercial, and policy-making contexts.

Social Media Analytics, Sentiment Analysis, Sarcasm Detection, User Engagement, Natural Language Processing (NLP), Machine Learning, Multimodal Analysis, Deep Learning for Social Media

"Analyzing Public Mood and Narrative Flow: A Deep Learning Framework for Archived Social Media Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.51-64, December-2025, Available :http://www.jetir.org/papers/JETIRHE06007.pdf

"Analyzing Public Mood and Narrative Flow: A Deep Learning Framework for Archived Social Media Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. pp51-64, December-2025, Available at : http://www.jetir.org/papers/JETIRHE06007.pdf