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@article{190426,
        author = {Adnan Khan and Ganesh Kalapad and Aniket Nagare and Suhas Dube and Dr. Shital Gaikwad},
        title = {Real-Time News & Social Media Sentiment Analyzer + Summarizer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5305-5309},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190426},
        abstract = {The rapid increase in online news content and the spread of misinformation have made it difficult for users to identify reliable information, understand public sentiment, and consume lengthy articles efficiently. To address these challenges, this project introduces an intelligent real-time news analysis system that integrates news fetching, automatic summarization, sentiment analysis, and fake news detection into a unified platform. The system collects news articles from the web using automated scraping methods and processes them through advanced NLP models to generate concise and meaningful summaries, enabling users to understand key information at a glance.
Sentiment analysis is performed using the VADER sentiment model, which evaluates the emotional polarity of the news content and classifies it into positive, negative, or neutral categories, providing insights into public mood and media tone. To ensure information reliability, a machine-learning–based fake news detection module is incorporated, which analyzes linguistic patterns and article characteristics to determine whether the fetched news is credible or suspicious.
The platform is designed with a clear and user-friendly interface that presents summaries, sentiment scores, and fake news predictions in real time, significantly reducing cognitive load and helping users make quick, informed decisions. By combining multiple analytical components into a single system, this project enhances the accuracy, trustworthiness, and accessibility of digital news consumption.},
        keywords = {Abstractive Summarization, BART, T5, Natural Language Processing, Sentiment Analysis, Fake News Detection, Real-Time News Analysis.},
        month = {January},
        }