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@article{178677,
        author = {Arya Shende and Aditya Kakade and Aditya Patil and Shreyash Parve},
        title = {Cloud-Based Sentiment Analysis using VADER & Amazon Comprehend},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4731-4735},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178677},
        abstract = {With the explosion of online textual content across social media, reviews, and forums, understanding public sentiment has become essential. This project proposes a hybrid sentiment analysis approach using two tools: VADER, a rule-based model effective for short informal texts, and Amazon Comprehend, a machine learning-based cloud service. By leveraging the strengths of both models and mitigating their limitations, the system offers a more comprehensive sentiment analysis solution. The entire pipeline is deployed on AWS, utilizing services like Lambda for processing and QuickSight for real-time sentiment visualization. This hybrid model aims to provide accurate, scalable, and insightful sentiment analysis for practical applications.},
        keywords = {},
        month = {May},
        }