Scrape full article text, headlines, authors, publication dates, entity tags, and sentiment signals from Economic Times, Mint, Hindu BusinessLine, NDTV, Reuters, Bloomberg, and 50,000+ news sources globally. Structured, real-time news data for media monitoring, financial intelligence, and NLP training datasets.
News data scraping is the automated collection of structured article data from online news publishers, wire services, and digital media outlets. A news article is more than text — it is a structured object: headline, subheadline, author, publication datetime, section classification, full body text, image URLs, canonical URL, word count, related article links, and in enriched pipelines, entity tags (companies, people, locations), topic classifications, and sentiment scores. Collecting this at scale — across thousands of sources, continuously — creates the structured news intelligence layer that powers media monitoring, financial research, and AI language model training.
India's news landscape is particularly rich and linguistically diverse. Alongside dominant English-language business publishers like Economic Times, Mint, and Hindu BusinessLine, there are significant regional vernacular publishers across Hindi, Tamil, Telugu, Bengali, and other languages that carry material news not covered by the English press. DataFlirt covers both the English business and general news tier and selected vernacular publishers, giving clients complete coverage of the Indian news ecosystem.
For financial intelligence use cases, news timeliness is critical. A regulatory announcement, an earnings surprise, or a geopolitical development can move markets within seconds of publication. DataFlirt's news collection infrastructure is designed for low-latency capture — monitoring RSS feeds, sitemaps, and publisher APIs continuously, with new articles typically collected within minutes of publication from major sources.
AI and NLP training data is a growing demand driver for news scraping. Large language models, sentiment classifiers, named entity recognition systems, and document summarisers all require large corpora of high-quality, diverse text data for training and fine-tuning. News articles — consistently structured, professionally written, and covering diverse topics and domains — are among the highest-quality text corpora available. DataFlirt can build custom news corpora to specification: by language, domain, date range, publisher tier, or topic category.
Comprehensive extraction built for reliability, accuracy, and scale.
Extract complete article body text, headline, subheadline, author, publication datetime, section, word count, and images from any news publisher.
Monitor news in 150+ languages across 50,000+ publishers — from global wire services to regional Indian vernacular outlets.
Automated sentiment scoring at article and entity level using fine-tuned classification models calibrated for financial and general news domains.
NLP-powered extraction of companies, people, locations, and organisations mentioned in articles — linked to standard identifiers where available.
Continuous source monitoring delivers new articles within minutes of publication, with keyword and entity-based alert delivery via webhook.
Track article volume, sentiment trends, and share of voice for any topic or entity over time — surfacing emerging coverage patterns.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data — reliably.
{ "status": "success", "source": "economictimes", "scraped_at": "2025-03-20T11:30:00Z", "article": { "headline": "RBI holds repo rate at 6.5% for seventh consecutive meeting", "author": "Siddharth Upasani", "published": "2025-03-20T10:15:00+05:30", "section": "Economy", "word_count": 842, "entities": ["RBI","Sanjay Malhotra","MPC"], "sentiment": "neutral", "url": "https://economictimes.indiatimes.com/..." } }
Built on proven open-source tools and cloud infrastructure — no vendor lock-in.
RSS and sitemap polling combined with publisher API monitoring ensures new articles are captured within minutes of publication.
spaCy and HuggingFace transformer models perform NER, topic classification, and sentiment scoring on every collected article.
Article extraction and basic NLP enrichment in 150+ languages. Deep NLP (sentiment, NER) with highest accuracy for English, Hindi, and major European languages.
Extracted company and person entities linked to standard identifiers — stock tickers, CINs, Wikipedia entries — for cross-source analytical consistency.
Kafka-backed streaming pipeline delivers articles to downstream consumers in real time as they are collected and enriched.
Publisher-specific strategies for subscription content where client holds a valid subscription — enabling full-text extraction beyond metered paywalls.
From solo analysts to enterprise data teams — here's how organizations use this data.
Every industry is affected by news — regulatory changes, competitive moves, market developments, reputational events. DataFlirt delivers structured, entity-tagged, sentiment-scored news data continuously across 50,000+ global sources — so your brand monitoring, financial intelligence, and AI training pipelines are always working from a complete, current picture of the information landscape.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
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For large organizations with custom requirements.
Everything you need to know before getting started.
Join data teams worldwide using DataFlirt to power products, research, and operations with reliable, structured web data.
