PulseBit NewsSentiment API
From chaos to clarity — the world's sentiment, one API call away.
Core Value
PulseBit transforms the global news stream into structured, multilingual sentiment intelligence — headline · summary · sentiment · entities · topic · confidence — powered by GPT-4o-mini, XLM-RoBERTa, and SentenceTransformers.
Backed by multi-source ingestion (Google News, GDELT, Benzinga, NewsData.io + regional feeds), quality scoring, freshness filters, and MinHash deduplication for clean, production-ready insights.
Real-Time Performance
Built by L.O.J Enterprise Ltd (UK Reg. No. 12184909)
More insightful than legacy news APIs. A fraction of the cost.
Why Teams Choose PulseBit
Unified feed — news + summary + sentiment in one call
Proven scale — 50K+ requests/day processed
Accuracy validated — FinBERT & DistilBERT-multilingual benchmarks
Explainable AI — confidence, keywords, entities, reasoning
Developer-first — setup < 3 minutes
SLA-backed reliability — 99% uptime (Pro+)
Global-ready — 15+ languages detected
How It Works — The AI Stack Behind PulseBit
| Layer | Model/Logic | Purpose |
|---|---|---|
| Hybrid Ingestion Layer | Google News RSS · GDELT TSV · Benzinga XML · NewsData.io · Curated Editorial Feeds (BBC, Bloomberg, Guardian, Wired, TechCrunch, Nature) · 8 Regional Feeds | Real-time multi-source global aggregation |
| Feed Parsing Engine | feedparser · requests · xmltodict | Handles RSS/XML/TSV variability |
| Normalisation Engine | normalise_article() | Cleans structure, timestamps, metadata |
| Freshness Filter | is_fresh() | Ensures all articles < 3 hours old |
| Language Detection | lightweight heuristic (with optional langdetect) | Identifies language for multilingual handling |
| Quality Ranking Engine | calculate_quality_score() | Weights credibility, freshness, confidence |
| Source Balancing | sample_sources_weighted() | Prevents over-representation of any source (e.g., Bloomberg) |
| Summarisation Model | GPT-4o-mini | Factual 1–2 sentence summaries |
| Sentiment Analysis | GPT-4o-mini (structured JSON) | Label, score, confidence, keywords, entities, explanation |
| Entity Extraction | Davlan/xlm-roberta-base-ner-hrl | Multilingual ORG/PER/LOC extraction |
| Embeddings & Semantic Search | SentenceTransformers (all-MiniLM-L6-v2) | Hybrid keyword + semantic retrieval |
| Deduplication Layer | PostgreSQL ON CONFLICT DO NOTHING + MinHash LSH | Removes duplicate and near-duplicate articles |
| Caching Layer | Redis | Sub-2s latency, rate-limited protection |
| Persistent Storage | PostgreSQL | Structured news intelligence at scale |
Features · Advantages · Benefits
| Feature | Advantage | Benefit |
|---|---|---|
| Real-time news feed | 2–5 min average freshness | React to breaking events early |
| Multilingual support | 15+ languages supported | Expand global market coverage |
| Sentiment scoring | GPT-4o-mini structured output | Trust decisions in production |
| Semantic search | MiniLM embeddings + keyword | Find nuanced topics, not just keywords |
| Entity extraction | People, orgs, locations | Build relationship graphs |
| Historical data | Growing daily dataset | Backtest models with expanding history |
| Explainability | Confidence + reasoning | Debug/improve ML pipelines |
| Developer-first API | RESTful, documented, <3min setup | Ship faster with zero friction |
Endpoint Ecosystem Overview
News Intelligence
Historical Analytics
Dataset Export
Sentiment
Live Stream
Example Response
{
"status": "success",
"query": "Tesla",
"count": 42,
"articles": [
{
"title": "Tesla unveils new battery technology at investor day",
"summary": "Tesla announced breakthrough in battery efficiency, promising 400-mile range.",
"sentiment_score": 0.78,
"sentiment_label": "POSITIVE",
"confidence": 0.92,
"source": "Reuters",
"published": "2025-11-14T09:23:00Z",
"topic": "tech",
"entities": [
"Tesla",
"Elon Musk",
"battery"
],
"link": "https://example.com/article"
}
],
"summary": {
"overall_avg_sentiment": 0.45,
"total_articles": 42,
"top_entities": [
{
"entity": "Tesla",
"mentions": 38,
"avg_sentiment": 0.52
},
{
"entity": "Elon Musk",
"mentions": 24,
"avg_sentiment": 0.31
}
]
}
}Pricing & Rate Limits
Basic
- Dataset Mode
- Historical
- Sentiment
- News
Pro
- Dataset Mode
- Historical
- Sentiment
- News
- Stream
Who PulseBit Is Built For
Fintech & Trading Teams
Integrate sentiment signals into algorithmic trading strategies. Backtest with 365 days of historical data. Monitor market-moving events in real time.
ML Engineers & Data Scientists
Train models on clean, labeled datasets. Export daily/weekly feeds. Use embeddings for semantic clustering and topic modeling.
Media & Research Analysts
Track brand sentiment across 15+ languages. Analyze emerging narratives. Generate automated reports with confidence scores.
Academic Researchers
Study sentiment trends over time. Access explainable AI outputs. Publish findings backed by production-grade data.
Developers & Startups
Ship sentiment features in < 3 minutes. Scale from prototype to production. Pay only for what you use.
Trust & Transparency
Company & Support
Integrations
Case Study
Fintech Analytics Team Simplifies Workflow and Cuts API Spend by Consolidating Vendors
Quantitative Analytics Team · London, UK
(Modelled real-world scenario based on PulseBit's actual performance metrics, pricing, and industry-typical tooling.)
Scenario: A small London-based quantitative analytics team originally relied on NewsAPI for raw headlines, a legacy enterprise sentiment provider (formerly AYLIEN, now Quantexa), custom Python scripts for NER and semantic grouping, and manual CSV processes for building historical trends. This setup was expensive, slow, and required maintaining 3–4 separate tools.
Challenges with the Old Setup:
- No unified sentiment + summary + entity extraction
- No multilingual coverage
- No clean historical endpoint for backtesting
- Per-provider limits
- Different schemas across vendors
- Slower integration and maintenance
- High monthly cost, especially for sentiment APIs
Solution (Modelled): The team consolidated everything into PulseBit Pro ($19.99/mo): /news_recent for real-time enriched news, /news_search + /news_semantic for ticker/topic tracking, /news_search_summary for instant sentiment signals, /historical/cache for quick backtesting, /dataset/daily_dataset for ready ML datasets, and /sentiment for custom sentiment analysis. This replaced 3 separate vendors with one unified API.
Cost Efficiency:
Previous combined cost estimate: NewsAPI + enterprise sentiment provider + custom infra = $500–$2,400/month (typical range)
With PulseBit Pro: $19.99/month (10,000 requests, all endpoints: news, sentiment, historical, datasets, stream)
→ 60–90% cost reduction depending on previous setup
Integration Speed:
Before PulseBit
1–2 weeks
Multiple APIs + custom pipelines
After PulseBit
< 2 hours
Unified JSON + code samples
Quality Improvements: PulseBit uses GPT-4o-mini for sentiment + reasoning and summarisation, XLM-RoBERTa for multilingual NER, MiniLM-L6 for semantic embeddings, MinHash for deduplication, plus weighted source sampling, freshness filter (≤ 3 hours), and language + timestamp normalisation.
Result (modelled): More accurate sentiment, richer metadata (entities, keywords, explanation), better multilingual reach, and cleaner ML datasets.
Backtesting Efficiency: Using /historical + /dataset endpoints delivered faster dataset retrieval, less manual CSV work, smaller pipelines with fewer moving parts, and simpler modelling workflow.
"PulseBit removed the headache of juggling multiple APIs. The unified schema and built-in explainability made our models far easier to trust and deploy."
— Senior Analyst, Quantitative Analytics Team
Coming Soon (Roadmap)
Real-time Stream Visualizer
Phase 2WebSocket dashboard with live sentiment updates
Source Bias Metrics
Phase 2Left/right/center classification per article
Arabic/Hindi/Swahili Support
Q1 2026Expand language coverage to 25+
Open Developer Datasets
Q2 2026Public benchmark datasets for research
Have a feature request? Email us at apisupport@lojenterprise.com
Frequently Asked Questions
Everything you need to know about the PulseBit API
What endpoints are available in the PulseBit API?
PulseBit offers 17 endpoints across 5 categories: News Intelligence (8 endpoints including /news/recent, /news/search, /news/semantic), Historical Analytics (3 endpoints), Dataset Export (3 endpoints in CSV/JSON/Parquet), Sentiment Analysis (1 endpoint), and Live Stream (2 WebSocket endpoints).
How fresh is the sentiment data?
PulseBit processes news articles with 2-5 minute freshness. Our hybrid ingestion engine pulls from multiple sources (Google News, GDELT, Benzinga, NewsData.io) and enriches articles with GPT-4o-mini powered sentiment analysis in near real-time.
What languages does the API support?
The API supports 15+ languages with global multi-region coverage. Our XLM-RoBERTa and SentenceTransformers models provide multilingual sentiment analysis across major world languages.
How much does the API cost?
PulseBit is available on RapidAPI with 4 tiers: Basic (Free - 250 requests/month), Pro ($19.99/month - 10K requests), Ultra ($49.99/month - 50K requests), and Mega ($99/month - 200K requests). All tiers include access to all 17 endpoints.
What is the average API response time?
Our average response time is 1.4 seconds with 97% uptime. We maintain this performance while processing 1,000+ articles daily through our optimized pipeline with quality scoring and deduplication.
Can I use PulseBit for financial trading signals?
Yes, PulseBit is designed for fintech and quant teams. Our API provides structured sentiment data with entities, topics, confidence scores, and quality rankings that can be used for trading signals, market monitoring, and backtesting strategies.
Is there a free tier to test the API?
Yes, the Basic plan on RapidAPI is completely free with 250 requests per month. This allows you to test all endpoints and integrate PulseBit into your workflow before upgrading to a paid tier.
What AI models power the sentiment analysis?
PulseBit uses a 14-layer AI stack including GPT-4o-mini for headline/summary generation, XLM-RoBERTa for multilingual sentiment classification, SentenceTransformers for semantic embeddings, and MinHash for content deduplication.