Financial News Sentiment Analyzer

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.

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

Source	Type	Data	Cost
Yahoo Finance	RSS feed	Headlines, summaries, per-ticker news	Free
Google News	RSS feed	Aggregated headlines from 80,000+ sources	Free
SEC EDGAR	API	8-K, 10-K, 10-Q, Form 4 filings	Free
AI Search	API	AI-curated financial articles missed by RSS	$0.03/search
Deep Search	URL scraper	Full article text from any URL (CNBC, Reuters, etc.)	$0.05/page

What data do you get?

Each article in the output dataset contains:

Field	Description	Example
title	Article headline	"Top Analysts See Solid Upside in Nvidia Stock"
source	Source identifier	google_news, sec_edgar, ai_search
sentiment_label	Bullish / Bearish / Neutral	"bullish"
sentiment_score	Continuous score (-1.0 to +1.0)	0.850
sentiment_confidence	Model confidence (0.0 to 1.0)	0.95
composite_score	Score × credibility × confidence	0.547
sentiment_model	Which model produced the score	"vader" or "llm"
tickers	Mentioned stock tickers	["NVDA"]
companies	Resolved company names	["Nvidia"]
sectors	Sector classification	["Technology"]
industry	Industry classification	"Information Technology"
source_credibility	Source reliability score	0.70
published_at	Publication timestamp (UTC)	"2026-03-21T07:10:39Z"
language	Detected language	"en"
article_type	Category	"news", "filing", "press_release"
mentioned_tickers	Additional tickers found in text	["AMD", "INTC"]

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

Field	Description	Example
current_price	Live stock price	172.70
price_change_pct	Change from previous close	-3.28
market_cap	Market capitalization (USD)	4197473583104
pe_ratio	Trailing P/E ratio	35.24
short_ratio	Short interest ratio	1.32
earnings_date	Next earnings date	"2026-05-20"

Pricing

Event	Price	When charged
Article analyzed (VADER)	$0.02	Per article with fast rule-based sentiment
Article analyzed (LLM)	$0.05	Per article with AI-powered sentiment
AI Search query	$0.03	Per search query when AI Search source is enabled
Deep Search page	$0.05	Per URL scraped for full-text extraction
Ticker enrichment	$0.01	Per unique ticker enriched with live market data

Cost examples:

Use case	Configuration	Est. cost
Quick check, 1 ticker, 10 articles	VADER, Google News	~$0.21
Daily monitoring, 5 tickers, 50 articles	VADER, all free sources	~$1.01
Deep analysis with market data	LLM, AI Search, enrichment, 3 tickers	~$1.63
Full stack, 5 tickers, 50 articles	LLM, AI Search, Deep Search, enrichment	~$3.21

Compare to alternatives:

Provider	Annual cost	Sentiment	Market data	API access
Bloomberg Terminal	$24,240	Limited	Yes	No
Refinitiv Eikon	$22,000	Yes	Yes	Yes
Finnhub Premium	$6,000	Basic	Yes	Yes
This Actor (VADER)	~$120-600	Yes	Optional	Yes
This Actor (LLM)	~$300-1,500	Advanced	Optional	Yes

How it works

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

	VADER	LLM (AI-Powered)
Speed	Instant (~1ms/article)	~500ms/article (batched)
Cost	$0.02/article	$0.05/article
Strengths	Fast, deterministic, no external dependency	Understands context, negation, financial nuance
Weakness	Misses context in complex headlines	Slightly slower, higher cost
Best for	High-volume monitoring, cost-sensitive	Investment decisions, research reports

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

Parameter	Type	Default	Description
tickers	string[]	["AAPL", "TSLA"]	Stock tickers to analyze
query	string	—	Free-text search query
sources	string[]	All free sources	Which sources to scrape
maxArticles	integer	20	Max articles per source
sentimentModel	string	"vader"	"vader" or "llm"
includeRawText	boolean	true	Include article summaries
enrichWithMarketData	boolean	false	Add live price, market cap, P/E per ticker ($0.01/ticker)
deepSearchUrls	string[]	—	URLs for full-text extraction ($0.05/page)

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 = new ApifyClient({ 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.