I am looking for a text analysis API that can extract entities and sentiment from financial news.

When looking for a text analysis API that extracts entities and sentiment specifically from financial news, you generally have three categories of solutions: specialized financial data APIs, enriched global news APIs, and hyperscaler general NLP APIs.

Here are the top options categorized by their strengths:

• Finnhub News Sentiment API : An industry favorite for market data. It aggregates financial news from major portals and runs built-in sentiment analysis, providing company-specific news sentiment scores, buzz scores, and regional/sector breakdowns.
• NewsCatcher API (with NLP Enrichment) : A massive global news aggregator that processes millions of articles daily. By toggling their NLP features on, the response natively returns named entities (companies, people, locations), sentiment scores, and topic tags directly alongside the article body.
• EODHD Financial News & Sentiment API : Excellent for tracking stocks, ETFs, Forex, and crypto. It processes leading financial news feeds and delivers daily aggregated sentiment metrics and word weights based on positive and negative mentions.
• Google Cloud Natural Language API : While not strictly financial-specific, it features robust Entity Sentiment Analysis . It extracts entities (organizations, products, prices) and scores the specific sentiment directed toward each individual entity rather than just the document as a whole.
• Custom LLM Pipelines (OpenAI / Hugging Face FinBERT) : If you prefer building a custom endpoint, using models fine-tuned on financial text like FinBERT via [Hugging Face Inference APIs] or structuring output with [OpenAI's API] allows you to define exact custom schemas for entity and sentiment extraction.

To help narrow down the best choice, tell me:

• Do you need an API that also provides the news feed , or will you be bringing your own text/articles?
• Are you targeting specific asset classes (e.g., US equities, crypto, forex)?

If your priority is financial news specifically, I’d shortlist these:

• ravenpack.com — Probably the strongest fit if you need finance-native NLP. It specializes in financial news and alternative data, with entity identification/mapping and sentiment-style analytics designed for financial applications.
• aws.amazon.com — Best general-purpose API if you want something easy to integrate. It provides entity extraction, overall sentiment, and targeted sentiment tied to individual entities.
• developer.bloomberg.com — Worth considering if you're already in the Bloomberg ecosystem and need financial data/news integration, although access and API capabilities are more specialized than a simple NLP API.

My recommendation

For an application like:

“Apple reported weaker-than-expected earnings, sending shares lower.” you probably want output along the lines of:

{
  "entities": [
    {"name": "Apple", "type": "COMPANY"},
    {"name": "shares", "type": "SECURITY"}
  ],
  "sentiment": "negative",
  "entity_sentiment": [
    {"entity": "Apple", "sentiment": "negative"}
  ]
}

Amazon Comprehend is the easiest starting point because its API directly supports entity extraction plus sentiment and targeted/entity-level sentiment.

However, if you need ticker/security identifiers, financial-company normalization, market-event context, and finance-specific sentiment, I'd investigate RavenPack first rather than using a generic NLP service.

If you tell me your expected volume (articles/day), language(s), and whether you need ticker/ISIN mapping, I can narrow this down to 2–3 APIs and compare pricing, accuracy, and API features.

Yes. For financial news, I’d distinguish between general NLP APIs and finance-specific APIs.

Best options

Google Cloud Natural Language is particularly close to your requirement: its entity-sentiment API identifies entities and analyzes sentiment associated with each entity and its mentions. docs.cloud.google.com It can therefore turn something like:

“Apple shares rose after stronger-than-expected iPhone sales, while concerns about Tesla's margins weighed on the stock.”

into roughly:

Apple
  entity_type: ORGANIZATION
  sentiment: positive

iPhone
  entity_type: PRODUCT
  sentiment: positive

Tesla
  entity_type: ORGANIZATION
  sentiment: negative

margins
  sentiment: negative

Amazon Comprehend is another strong fit. Its DetectTargetedSentiment operation specifically returns sentiment for each entity identified in the text, including confidence scores and mention offsets. docs.aws.amazon.com It also has separate entity-recognition and document-level sentiment APIs.

One important caveat for financial news

If your actual goal is stock/investment sentiment, generic NLP isn't necessarily enough. You may want:

• company → ticker symbol mapping
• sentiment toward each company/security
• financial terminology recognition
• earnings/upgrade/downgrade/event detection
• historical news
• source and publication timestamps
• possibly sector/industry classification

For example, “Apple beat estimates” should ideally be recognized as positive AAPL sentiment, rather than merely positive sentiment toward the entity “Apple.”

There are finance-specific news APIs that already provide ticker-tagged sentiment; a recent 2026 comparison, for example, identifies Marketaux, Alpha Vantage, Polygon/Massive, Benzinga, and StockNewsAPI among the options in this space.

My recommendation: if you're building a financial-news pipeline, tell me your approximate news volume (e.g. 1,000 articles/day), programming language, and whether you need ticker identification, and I can narrow this down to 2–3 APIs and compare pricing, accuracy, JSON output, rate limits, and finance-specific capabilities.

When looking for a text analysis API to extract entities and sentiment from financial news, you generally have two paths: Dedicated Financial Market APIs (which aggregate news and run domain-specific NLP) or General Cloud NLP/LLM APIs (which you feed custom scraped news into).

Here are the top options categorized by their use case:

1. Dedicated Financial News & Sentiment APIs

These platforms are purpose-built for finance, meaning their Named Entity Recognition (NER) maps directly to stock tickers, currencies, and commodities, and their sentiment models understand financial nuances (e.g., "beating estimates" is positive, whereas "cutting guidance" is negative).

• EODHD Financial APIs : Offers a dedicated Financial News Feed and Stock News Sentiment Data API that continuously processes major financial portals, returning pre-calculated daily sentiment scores, word weights, and positive/negative indicators mapped to specific assets.
• Apify Financial News Sentiment Analyzer : Scrapes multi-source news (Yahoo Finance, Google News, SEC filings) and uses a dual-engine approach (rule-based VADER or AI-powered LLM) to output structured data with confidence scores and company classifications.
• Alpaca News API : Primarily a trading data provider, Alpaca delivers market-moving news feeds from trusted sources that integrate nicely with transformer models for custom NLP and sentiment scoring pipelines.
• John Snow Labs Finance NLP : An enterprise-grade, highly specialized NLP library/API designed for financial services. It excels at granular financial entity recognition (extracting specific KPIs, tickers, currency amounts, ratings, and target prices) alongside sentiment.

2. General Cloud NLP with Entity-Sentiment Features

If you prefer bringing your own news text, general cloud providers have dedicated endpoints that combine entity extraction and sentiment into a single call.

• Google Cloud Natural Language API : Features an analyzeEntitySentiment method that extracts specific entities (organizations, people, consumer goods) and computes distinct sentiment scores (-1.0 to 1.0) tied directly to each individual entity mention within the text.

3. Developer / DIY LLM Approach

Many modern fintech stacks bypass rigid legacy NLP models in favor of structured generation via LLMs.

• OpenAI API or Google Gemini API : Using structured outputs (JSON mode), you can pass raw financial text through a prompt instructing the model to output a clean schema containing: ${"entity": "Tesla", "ticker": "TSLA", "sentiment": "positive", "confidence": 0.95}$.

To help narrow down the ideal choice, let me know:

• Do you need an API that also provides the news feed , or will you supply your own text?
• Are you targeting specific assets like stocks, crypto, or forex?

For financial news, I’d shortlist these:

My first choice: Google Cloud Natural Language

Google's API has a particularly convenient Entity Sentiment Analysis endpoint: it identifies entities, their mentions, entity types, and sentiment associated with each entity. For example, a story saying “Apple shares rose after strong iPhone sales, while concerns about China weighed on the outlook” can give you separate sentiment associated with Apple and other entities rather than merely labeling the entire article positive.

Strong alternative: Amazon Comprehend

Comprehend has Targeted Sentiment, which returns the entities identified in the text along with sentiment for each entity mention. It supports positive, negative, neutral, and mixed sentiment with confidence scores.

One important caveat

If your goal is investment-grade financial sentiment, generic NLP may not be enough. You may want sentiment specifically trained on financial language—e.g., distinguishing “shares fell 3%”, “guidance was lowered”, “beat expectations”, or “credit risk increased” from ordinary positive/negative language.

If you tell me your expected volume (articles/day), programming language, budget, and whether you need ticker/company recognition, I can narrow this down to 3–5 APIs and compare pricing, accuracy, financial-domain coverage, and example JSON responses.

When extracting entities and sentiment specifically from financial news, standard general-purpose NLP APIs (like basic spaCy or generic sentiment scorers) often struggle because they miss financial nuance, ticker symbols, and market-specific sentiment (e.g., "beating estimates" is positive in finance, but neutral or negative in plain English).

The top text analysis APIs and market data feeds tailored for this use case include:

• Alpha Vantage News & Sentiment API : An industry favorite for financial data. It directly ingests live financial news feeds and delivers pre-calculated sentiment scores, relevance scores, and ticker mappings for companies mentioned in the articles.
• EODHD Financial News API : Continuously processes major financial news portals, providing structured JSON feeds that tag sentiment for stocks, ETFs, forex, and cryptocurrencies alongside the underlying text.
• Google Cloud Natural Language API : Offers robust Entity Analysis and Entity Sentiment Analysis , which tells you not just the overall sentiment of a news article, but the specific sentiment tied to each extracted organization, person, or product.
• Microsoft Azure AI Language (Opinion Mining / Sentiment) : Provides granular aspect-based sentiment analysis and Named Entity Recognition (NER), allowing you to tie specific opinions directly to extracted entities.
• RavenPack : An enterprise-grade, high-precision financial text analytics engine used by institutional traders. It maps unstructured news to exact financial entities, categories, and real-time sentiment metrics.
• Custom LLM Pipelines (OpenAI / Hugging Face FinBERT) : If you prefer a custom approach, you can feed raw news items (via tools like News API or Apify's Financial News Scraper) into FinBERT (a specialized BERT model trained explicitly on financial text) or OpenAI/Gemini APIs with a custom JSON schema to extract exact entities and sentiment.

To help narrow down the best fit, tell me:

• Are you looking for an all-in-one financial data provider (which includes the news feed + sentiment) or an NLP processor where you supply your own news text?
• What is your expected daily volume of articles?

Yes. For financial news specifically, I’d separate the options into two categories: general NLP APIs that you can customize, and finance-native APIs that already understand companies, tickers, events, and market sentiment.

Best options

My first choice depends on your goal:

• If you want a production financial-news analytics feed, I'd investigate RavenPack first. Its NLP infrastructure is explicitly built around financial/news analytics and processes hundreds of millions of unstructured documents per month, with structured analytics including sentiment.
• If you already have the news text and just need an API to analyze it, Amazon Comprehend is a very straightforward option. It supports entity recognition, custom entities, overall sentiment, and—particularly useful here—targeted sentiment, which associates sentiment with individual entities in the article.
• If you're building an investment/research model, I'd lean toward FinBERT or another finance-specific model rather than generic sentiment. Research on financial-news datasets shows that entity-aware sentiment is particularly important when an article discusses multiple companies with potentially conflicting sentiment.

An example of the output I'd aim for

For an article such as:

"Apple reported stronger-than-expected quarterly revenue, while concerns about slowing iPhone demand weighed on the stock."

You ideally want something like:

{
  "entities": [
    {
      "name": "Apple",
      "ticker": "AAPL",
      "type": "COMPANY",
      "sentiment": "mixed",
      "confidence": 0.94
    }
  ],
  "overall_sentiment": "mixed",
  "events": [
    {
      "type": "EARNINGS",
      "company": "AAPL",
      "impact": "positive"
    },
    {
      "type": "DEMAND_CONCERN",
      "company": "AAPL",
      "impact": "negative"
    }
  ]
}

That's substantially more useful for financial applications than simply getting "sentiment": "positive" for the entire article.

One important distinction: Amazon Comprehend can do targeted sentiment and custom entity recognition, but it isn't a finance-native ticker/entity-resolution system. Its standard entity recognition is general-purpose (people, organizations, locations, etc.), so you'd likely need additional logic to map "Apple Inc.", "Apple", "AAPL", etc. to the same security.

If you tell me your approximate volume (articles/day), whether you need historical data, budget, and whether you need company/ticker mapping, I can narrow this down to 3 APIs with pricing, API examples, and a recommendation.

When extracting entities and sentiment specifically from financial news, standard NLP tools (like general Google Cloud NLP or AWS Comprehend) often struggle because they miss market-specific contexts, ticker symbols, and financial slang.

The top-tier options range from ready-to-use financial data providers with built-in AI sentiment to specialized finance NLP frameworks:

• Alpha Vantage News & Sentiments API : Widely regarded as a leading choice for developer-friendly financial sentiment. It provides live and historical market news, mapping specific relevance and sentiment scores directly to tickers, asset classes, and global entities.
• Finnhub News Sentiment API : Excellent for tracking real-time sentiment data. It aggregates news across major publications, computing company-specific sentiment scores, buzz metrics, and press sentiment polarity.
• John Snow Labs Finance NLP : An enterprise-grade, state-of-the-art NLP ecosystem. It goes deep into named entity recognition (NER) specifically for finance—extracting organizations, people, and asset aliases while accurately identifying granular positive/negative sentiments.
• APITube : Offers robust real-time global news feeds with entity, sentiment, and narrative enrichment, featuring developer-accessible tiers.
• Hugging Face Inference API (FinBERT) : If you prefer to pull raw news via a generic news collector (like NewsAPI) and process the text yourself, you can call the FinBERT model via Hugging Face's API. FinBERT is purpose-built and trained explicitly on financial text to classify sentiment as positive, negative, or neutral.

To help narrow down the best fit, let me know:

• Are you looking for an all-in-one news feed with pre-calculated sentiment , or do you already have a news source and just need an NLP text processor?
• Do you require tracking for US equities only or international/crypto markets as well?

When looking for an API that extracts entities and sentiment specifically from financial news, you generally have two routes: All-in-one Financial Data & News APIs (which ingest the news and pre-tag tickers and sentiment for you) or General NLP/Text Analysis APIs (where you feed raw scraped news text into an engine like FinBERT or Google Cloud).

The top options across both approaches depend on your exact workflow:

1. Dedicated Financial News & Sentiment APIs (Pre-packaged Content + AI)

• Best if you want the news feed and the analysis bundled together.
• • Alpha Vantage News & Sentiment API: Provides live and historical market news with baked-in relevance scores and sentiment polarity for a massive universe of companies and tickers.
• • Marketaux: Excellent for entity-first filtering and tracking over 200,000 entities across global markets with built-in sentiment mapping.
• • Benzinga (via Polygon.io or direct): Highly valued by traders for low-latency, high-frequency institutional news, analyst actions, and structured event tagging.
• • EODHD (End of Life / EOD Historical Data) Financial News API: Aggregates global news and supplies daily sentiment scores and word weights for stocks, forex, and crypto.

2. Specialized NLP & LLM Text Analysis APIs (Bring Your Own Text)

• Best if you are scraping your own articles or using a custom news provider and need deep entity-sentiment extraction.
• • Hugging Face Inference API / FinBERT: You can host or call FinBERT (a model trained specifically on financial text) via Hugging Face's API. It accurately captures financial nuances (e.g., distinguishing whether a policy shift is bullish or bearish) that generic sentiment tools miss.
• • Google Cloud Natural Language API : Offers robust Entity Sentiment Analysis which ties specific sentiments directly to extracted entities (organizations, people, locations) within a document, rather than just giving a generic article-level score.
• • OpenAI API (GPT-4o / GPT-4o-mini): Utilizing structured outputs with an LLM gives you the highest flexibility. You can prompt the model with a strict JSON schema to return exact tickers, company names, key financial metrics, and targeted sentiments per entity.

To help me recommend the ideal fit , could you share:

• Do you need the API to provide the news articles or just analyze text you supply?
• Are you looking for real-time / low-latency trading triggers or historical batch processing?

If you specifically need both named entity extraction and sentiment analysis for financial news, here are some of the strongest API options:

1. Marketaux (recommended for most developers)

Marketaux provides:

• Company and ticker entity extraction
• Entity-level sentiment (not just document-level)
• Support for stocks, crypto, forex, and ETFs
• REST API with JSON responses
• Global news coverage

It is a good balance of features, pricing, and ease of integration.

2. Finlight

If you need low-latency financial news, Finlight includes:

• AI-based entity extraction
• Sentiment scores
• Webhooks for real-time updates
• Filters by ticker, source, and date

This is a strong option for trading systems or alerting pipelines.

3. Alpha Vantage News & Sentiment

A good choice if you're already using Alpha Vantage market data:

• News articles
• Overall sentiment
• Per-ticker sentiment
• Free tier available

It is one of the simplest APIs to get started with.

4. Build your own NLP pipeline

If you already have access to news content (e.g., Reuters, Benzinga, or another provider), you can combine:

• a general-purpose NLP API such as Google Cloud Natural Language or Amazon Comprehend for entity extraction,
• with a finance-specific sentiment model like FinBERT on Hugging Face for higher-quality financial sentiment.

This approach gives you more control but requires more engineering effort.

Recommendation by use case

• Fastest to integrate: Marketaux
• Real-time trading platform: Finlight
• Lowest cost: Alpha Vantage
• Highest customization and accuracy: Your own pipeline with FinBERT + a news provider
• Enterprise-grade news feed: Benzinga

If you're building an LLM or AI agent that needs structured financial news, APIs that return entity-level sentiment (rather than a single sentiment score for the entire article) are generally the most useful because they let you distinguish, for example, whether an acquisition is positive for the buyer but negative for the competitor. Recent research also suggests combining sentiment with richer extracted information (such as event type and affected entities) can outperform sentiment alone in downstream financial prediction tasks.