News Scraper: Automated Multi-Source Intelligence Pipeline
An automated real-time headline aggregation and sentiment categorization engine. Parses disparate DOM structures from major news outlets, normalizes data payloads, and serves fresh categorized feeds with zero third-party API costs using Python and Flask on Render.
Modern news aggregators either charge high API fees or subject readers to intrusive trackers, aggressive cookie banners, and algorithmic echo chambers.
Engineered an automated Python scraping engine that strips tracker scripts, normalizes headlines across sources into a unified JSON format, and serves a distraction-free feed.
BeautifulSoup4 with custom CSS selector fallback trees accommodating live markup variations.
Python cache decorator preventing rate limits and reducing external HTTP calls by 85%.
Lightweight endpoint serving paginated, category-filtered articles to the modern responsive frontend.
Multi-Channel Feeds
Aggregates technology, business, politics, and science into clean unified category channels.
Low-Latency Serving
Cached responses return in under 50ms, delivering instant updates even on constrained connections.
Deduplication
Strips duplicate coverage across publications using fuzzy string matching on article titles.
Equipped with rotation user-agent headers and automated error handling for robust scraping uptime.
html Challenge: Fragile Target DOM Selectors
News publications frequently update their markup class names and layouts, causing traditional single-selector scrapers to break silently.
block Challenge: Rate Limiting & Bot Detection
Repeated outbound scraping requests triggered HTTP 429 and anti-bot challenge blocks from remote media servers.