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@article{174509,
        author = {V.Jaya Sai Vardhan and P.A,S Shankar and Y.Madhuri and G.Raghu Ram and A.S.C.L Narasimha},
        title = {Echopulse: Decrypting Latent Perceptive Signals from Interactive Narratives},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4229-4234},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174509},
        abstract = {Sentiment analysis has emerged as a critical tool in understanding user perceptions, yet traditional models often fail to capture deeper cognitive states such as dissatisfaction, excitement, and neutrality. This paper introduces EchoPulse, an advanced sentiment analysis framework leveraging deep learning and Natural Language Processing (NLP) to decrypt latent perceptive signals from interactive narratives. The proposed system utilizes transformer-based architectures like BERT to detect subtle linguistic patterns, surpassing conventional machine learning techniques in accuracy. Additionally, real-time tracking capabilities provide evolving sentiment profiles, enabling businesses to optimize engagement strategies. By integrating speech-to-text processing and interactive visualization tools, EchoPulse ensures a dynamic, real-time sentiment analysis experience. This research highlights the significance of deep learning in multi-dimensional cognitive state detection and its potential applications in business intelligence and user experience optimization.},
        keywords = {Sentiment Analysis, Deep Learning, BERT, Natural Language Processing, Interactive Narratives, Real-time Sentiment Tracking},
        month = {March},
        }