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An Automated Sentiment-Driven News Summarization and Categorization System using Web Scraping and NLP

by Divya T.L., Aniketh S., Anup Ganesh M.S.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 81
Year of Publication: 2025
Authors: Divya T.L., Aniketh S., Anup Ganesh M.S.
10.5120/ijca2025924752

Divya T.L., Aniketh S., Anup Ganesh M.S. . An Automated Sentiment-Driven News Summarization and Categorization System using Web Scraping and NLP. International Journal of Computer Applications. 186, 81 ( Apr 2025), 27-31. DOI=10.5120/ijca2025924752

@article{ 10.5120/ijca2025924752,
author = { Divya T.L., Aniketh S., Anup Ganesh M.S. },
title = { An Automated Sentiment-Driven News Summarization and Categorization System using Web Scraping and NLP },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2025 },
volume = { 186 },
number = { 81 },
month = { Apr },
year = { 2025 },
issn = { 0975-8887 },
pages = { 27-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number81/an-automated-sentiment-driven-news-summarization-and-categorization-system-using-web-scraping-and-nlp/ },
doi = { 10.5120/ijca2025924752 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-04-26T02:19:43.106821+05:30
%A Divya T.L.
%A Aniketh S.
%A Anup Ganesh M.S.
%T An Automated Sentiment-Driven News Summarization and Categorization System using Web Scraping and NLP
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 81
%P 27-31
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents an automated system for summarizing and filtering news articles based on sentiment analysis. The system leverages Python-based tools to fetch news headlines using the Google News API, scrape article content using Beautiful Soup and Newspaper3k, and select optimal content through similarity scoring with the sentence-transformers/all-MiniLM-L6-v2 model. Summarization is performed using the Llama 3.2 3B model, while sentiment classification is achieved using the cardiffnlp/twitter-roberta-base-sentiment-latest model. The processed data is stored in Firebase and accessed via an Android app, enabling users to filter negative news and select preferred categories. The system processes 35–40 news articles in 10–11 minutes, significantly outperforming manual efforts. This approach enhances efficiency in news consumption while ensuring scalability across six categories: world, nation, business, technology, entertainment, and sports.

References
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Index Terms

Computer Science
Information Sciences

Keywords

News summarization sentiment analysis web scraping natural language processing Firebase Realtime Database.

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