Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{199294,
        author = {K Aishwarya and Megha H U and Kuchi Susmitha and Meghana K and Narendra Mani Tripathi},
        title = {Automated Crawling, Categorization and Sentiment Analysis of Digital News with Incorporated Feedback System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12304-12309},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199294},
        abstract = {The rapid growth of digital news platforms has increased the difficulty of monitoring large volumes of information published across multiple sources and languages. Manual tracking of such information is time-consuming and often inefficient, particularly when timely identification of important developments is required. This paper presents an automated multilingual news monitoring system designed to collect, categorize, and analyze digital news articles using natural language processing techniques and machine learning methods.
The proposed system retrieves news articles automatically from multiple RSS feed sources and processes them through a structured analysis pipeline. Sentence-transformer embeddings combined with logistic regression are used to categorize articles into thematic domains, while sentiment polarity is evaluated using the VADER sentiment analysis model. The system further identifies severity levels based on sentiment scores and groups related articles describing the same events using agglomerative clustering. A notification mechanism generates automated alerts when high-severity negative news is detected. An interactive dashboard is also provided to visualize sentiment trends, category distribution, and geographic information extracted from news articles.
The experimental results demonstrate that the proposed system provides an efficient framework for multilingual news monitoring and supports faster identification of critical developments through automated analysis and visualization.},
        keywords = {News Crawling, Natural Language Processing, Sentiment Analysis, Multilingual Classification, Event Clustering, News Monitoring System},
        month = {April},
        }