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@article{208138,
        author = {C. Jedidiah Solomon and Ms. A. V. Thangam},
        title = {Emotion-Aware Cloud Scaling: A Comprehensive Study on Preventing User Frustration Through Predictive Auto-Scaling},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {4},
        pages = {490-495},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208138},
        abstract = {This study examines Emotion-Aware Cloud Scaling (EACS), a novel approach that detects user frustration from text and voice inputs to pre-emptively scale cloud resources. Traditional CPU-based scaling reacts after performance degradation while users abandon sites. EACS analyses customer sentiment in real-time, scaling servers before technical problems occur. The study presents comprehensive analysis through two master tables comparing traditional methods vs EACS across all scenarios, industries, and business metrics. Results show 92% frustration reduction and 35% sales improvement.},
        keywords = {emotion-aware scaling, user frustration, sentiment analysis, predictive scaling, cloud computing, customer experience},
        month = {September},
        }