Financial News Sentiment Analyzer

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What does Financial News Sentiment Analyzer do?

Financial News Sentiment Analyzer scrapes financial news from multiple sources, runs AI-powered sentiment analysis on every headline, and outputs structured, machine-readable data enriched with live market data, confidence scores, and company classifications.

👁 Features

It is an affordable, pay-per-use alternative to Bloomberg Terminal ($24,240/year), Refinitiv Eikon ($22,000/year), and other institutional data providers. You pay $0.02 per article analyzed — no subscriptions, no minimums.

Key features:

• Multi-source aggregation — Yahoo Finance, Google News, SEC EDGAR filings, and AI-powered search in a single run
• Two sentiment engines — Fast rule-based (VADER) or premium AI-powered (LLM) with financial context understanding
• Live market data enrichment — Current price, price change %, market cap, P/E ratio, short interest, earnings dates per ticker
• Proprietary composite scoring — Sentiment weighted by source credibility and model confidence
• Deep Search — Full-text article extraction from any URL for superior sentiment accuracy
• Company & sector enrichment — Automatic ticker-to-company mapping with industry classification
• Language detection — 55+ languages detected, filter non-English noise automatically
• Budget protection — Automatically stops when your spending limit is reached, never overcharges
• Pay only for what you use — No monthly fees, no setup costs

Data sources

What data do you get?

Each article in the output dataset contains:

With market data enrichment enabled ($0.01/ticker):

Pricing

Cost examples:

Compare to alternatives:

How it works

👁 Architecture

Quick start

Step 1. Click Try for free and configure your run:

• Enter stock tickers (e.g., AAPL, TSLA, NVDA)
• Choose news sources (default: all free sources)
• Pick sentiment model — vader for speed, llm for accuracy
• Optionally enable market data enrichment

Step 2. Run the Actor.

Step 3. Download results from the Dataset tab as JSON, CSV, or Excel.

Sentiment models compared

Example where it matters:

"Nvidia Stock Drops. Not Even Elon Musk Can Give Shares a Boost."

• VADER: bullish +0.83 (confused by "Boost")
• LLM: bearish -0.95 (understands "Drops" is the signal)

Output example

{
"article_id":"cbb7559567eb174a",
"source":"google_news",
"source_url":"https://news.google.com/rss/articles/...",
"source_credibility":0.7,
"title":"Apple Inc. Stock Position Raised by Groupama Asset Management",
"summary":"Apple Inc. Stock Position Raised by Groupama Asset Management",
"authors":["MarketBeat"],
"published_at":"2026-03-22T11:17:21Z",
"scraped_at":"2026-03-22T14:30:12Z",
"tickers":["AAPL"],
"companies":["Apple Inc."],
"sectors":["Technology"],
"sentiment_label":"bullish",
"sentiment_score":0.850,
"sentiment_confidence":0.95,
"sentiment_model":"llm",
"composite_score":0.547,
"language":"en",
"language_confidence":1.0,
"article_type":"news",
"topics":[],
"topic_confidence":0.0,
"mentioned_tickers":[],
"current_price":247.99,
"price_change_pct":-0.39,
"market_cap":3644938780672,
"pe_ratio":31.35,
"industry":"Computers, Phones & Household Electronics",
"short_ratio":2.41,
"earnings_date":"2026-04-30"
}

Use cases

Algorithmic trading — Feed real-time sentiment signals into your trading system. The composite score combines sentiment, source credibility, and confidence into a single actionable number. Market data enrichment adds price context alongside sentiment.

Portfolio monitoring — Schedule daily runs across your holdings. Track sentiment trends over time. Detect negative news before it hits the price. Get notified when short interest or P/E ratios change.

Research & due diligence — Analyze sentiment around a company before investing. Compare sentiment across competitors in the same sector. Use Deep Search to extract full articles from any URL.

AI agents & automation — Structured JSON output works directly with AI agents, LangChain, and automation platforms. Schedule runs and pipe results into your workflow.

Fintech products — Build sentiment dashboards, alerts, and newsletters. The standardized schema makes integration straightforward across all sources.

Input parameters

Integration examples

Python

from apify_client import ApifyClient
client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("YOUR_USERNAME/financial-news-sentiment").call(
 run_input={
"tickers":["AAPL","NVDA","TSLA"],
"sources":["google_news","sec_edgar"],
"maxArticles":20,
"sentimentModel":"llm",
"enrichWithMarketData":True,
}
)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
 price = item.get("current_price","N/A")
print(f"{item['sentiment_label']:>7s}{item['sentiment_score']:+.2f} ${price} | {item['title'][:50]}")

JavaScript

import{ ApifyClient }from'apify-client';
const client =newApifyClient({token:'YOUR_API_TOKEN'});
const run =await client.actor('YOUR_USERNAME/financial-news-sentiment').call({
tickers:['AAPL','NVDA','TSLA'],
sources:['google_news','sec_edgar'],
maxArticles:20,
sentimentModel:'llm',
enrichWithMarketData:true,
});
const{ items }=await client.dataset(run.defaultDatasetId).listItems();
items.forEach(item=>{
 console.log(`${item.sentiment_label}${item.sentiment_score} $${item.current_price} | ${item.title}`);
});

FAQ

Is this financial advice? No. Sentiment scores are AI-generated and may be inaccurate. This tool is for informational and research purposes only. Always do your own due diligence.

How accurate is the sentiment analysis? VADER with our financial lexicon correctly classifies ~75-80% of financial headlines. The LLM model achieves ~90-95% accuracy on financial text, including complex headlines with negation and context.

What about paywalled sources (Bloomberg, WSJ)? We do not scrape paywalled content. All sources are publicly accessible or use official APIs. Use Deep Search URLs to extract articles from sites you have access to.

How fresh is the data? Articles are scraped in real-time when the actor runs. Schedule it hourly or daily for continuous monitoring. Market data enrichment provides live prices at the time of the run.

Can I use this with my AI agent or LLM? Yes. The structured JSON output is designed for programmatic consumption. It works directly with LangChain, AutoGPT, Claude, and any tool that accepts JSON data.

What happens if my budget runs out? The actor automatically stops processing and returns whatever articles it has already analyzed. You are never overcharged. Set a spending limit in the Apify Console to control costs.

What happens if a source is down? Each source is fetched independently. If one fails, the others still return data. Errors are logged in the run stats.

Legal disclaimer

This actor only scrapes publicly available data. Article summaries are limited to 200 characters for legal compliance — full article text is never stored or distributed. SEC EDGAR data is explicitly public domain. All other data is derived analysis (sentiment scores) which is protected as transformative use.

Not affiliated with Yahoo Finance, Google, SEC, or any news publisher.