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

Yes. For financial news, I’d prioritize an API that can do entity-level sentiment, rather than only giving one sentiment score for the entire article.

Best options

• aws.amazon.com — strongest general-purpose choice. Its DetectTargetedSentiment API identifies entities and returns sentiment for each entity mention, including Positive, Negative, Neutral, or Mixed, with confidence scores. AWS Documentation AWS Documentation It also supports standard entity recognition for organizations, people, locations, quantities, dates, etc.
• cloud.google.com — good simple API. Google provides entity sentiment analysis that combines entity extraction with sentiment associated with each entity, including per-mention and aggregate scores.
• azure.microsoft.com — good if you're already on Azure. Its text APIs include both Named Entity Recognition and sentiment analysis. Microsoft Learn Note that Microsoft's older Entity Linking feature is scheduled to retire in 2028, so I'd use NER/Foundry for a new implementation.

What I'd choose for financial news

Suppose your article says:

"Apple shares rose after the company reported stronger-than-expected earnings, while concerns about Tesla's margins weighed on the EV sector." Ideally, your API output would look roughly like:

{
  "entities": [
    {
      "text": "Apple",
      "type": "ORGANIZATION",
      "sentiment": "POSITIVE",
      "confidence": 0.94
    },
    {
      "text": "Tesla",
      "type": "ORGANIZATION",
      "sentiment": "NEGATIVE",
      "confidence": 0.91
    }
  ]
}

Amazon Comprehend is probably the easiest starting point because targeted sentiment is specifically designed to associate sentiment with individual entities rather than merely classifying the whole document.

One caveat: if you're doing serious financial NLP—ticker/entity linking (AAPL vs. Apple Inc.), earnings-event extraction, analyst ratings, price targets, sector classification, or finance-specific sentiment—I'd look beyond generic NLP APIs and consider a finance-specific NLP API/model.

If you tell me your expected article volume, programming language, and whether you need ticker mapping, I can narrow this down to 2–3 APIs and compare accuracy, pricing, latency, and integration effort.

When extracting entities (companies, tickers, executives) and sentiment from financial news, general-purpose NLP APIs often miss domain-specific nuances (like how a "rally" or "slump" alters context).

The top API choices vary depending on whether you want an all-in-one financial news provider with pre-computed NLP or a raw text processing NLP engine where you pipe in your own scraped news.

1. Dedicated Financial News & Data APIs (Pre-Enriched with NLP)

These providers ingest financial news globally and expose APIs that already include ticker mapping, named entity recognition (NER), and sentiment scores out of the box.

• NewsCatcher API: Offers robust NLP enrichment layers designed for news corpora. It provides automatic article-level sentiment scoring (split into title and content scores from -1.0 to 1.0), named entity extraction, and topic/taxonomy tagging at index time. Great if you need clean, pre-labeled metadata feeds.
• APITube IO: Provides real-time financial and crypto news feeds equipped with pre-processed sentiment analysis, entity tracking, and event detection endpoints tailored for quantitative research and tracking market movements.
• Enterprise Terminals (Bloomberg API / Refinitiv Data API): If you have enterprise access, terminal APIs offer the gold standard for entity-mapped news sentiment (e.g., specific sentiment attached strictly to ticker AAPL rather than a general company mention in the same paragraph).

2. General Cloud NLP APIs (Bring Your Own Text)

If you already have a news collection pipeline (using RSS feeds or generic news collectors) and just need to pass the raw text payload through an analysis endpoint:

• Google Cloud Natural Language AI: Excels at robust Entity Analysis and Sentiment Analysis. While it is not inherently fine-tuned on financial jargon like FinBERT, its entity-level sentiment features can pinpoint specific entities and evaluate the polarity of text surrounding them.
• Custom FinBERT Pipeline via Hugging Face Inference API: For maximum financial accuracy without paying enterprise data fees, you can pull financial news via a standard feed (like Yahoo Finance RSS or NewsAPI) and route the text strings through a hosted FinBERT model via the Hugging Face Inference API . FinBERT is specifically trained on financial vocabularies to handle positive/negative/neutral classifications accurately in market contexts.

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

• Are you planning to bring your own raw news text or do you need an all-in-one feed that fetches the news for you?
• Do you need entity-level sentiment (e.g., separating sentiment for Company A vs. Company B in the same article)?

For financial news, I’d prioritize entity-level sentiment rather than just document-level sentiment. For example, in “Apple shares rose after strong iPhone sales, while concerns remain about China,” you want sentiment attached separately to Apple, iPhone sales, and potentially China.

Best options

Google Cloud Natural Language is probably the simplest starting point. Its analyzeEntitySentiment API combines entity extraction with sentiment and returns sentiment for individual entity mentions, plus aggregated entity sentiment.

Amazon Comprehend is particularly attractive if you're already on AWS. Its targeted-sentiment API returns the entities, entity types, sentiment for each mention, confidence scores, and coreference groups.

One important caveat for financial news

Generic NLP models aren't necessarily financial-sentiment models. Financial language can invert the meaning of ordinary sentiment:

“Revenue fell less than expected.” A generic model may interpret “fell” negatively, whereas a financial model should recognize this as potentially bullish relative to expectations.

So if you're building something like a stock/news analytics pipeline, I'd look for an API that supports:

• Company/ticker/entity recognition
• Entity-level sentiment
• Financial sentiment rather than generic sentiment
• Events such as earnings, acquisitions, guidance changes, layoffs, upgrades/downgrades
• Numerical/financial entity extraction
• Confidence scores
• Entity linking to canonical companies/tickers
• Batch processing for large news feeds
• Low latency if you're processing news in real time

If you tell me your volume (e.g. 10K articles/day), languages, and whether you need ticker/company linking, I can narrow this down to 3–5 financial-specific APIs and compare pricing, accuracy, latency, and API examples.

Google Cloud Natural Language is probably the simplest starting point. Its analyzeEntitySentiment API combines entity extraction with sentiment and returns sentiment for individual entity mentions, plus aggregated entity sentiment.

Amazon Comprehend is particularly attractive if you're already on AWS. Its targeted-sentiment API returns the entities, entity types, sentiment for each mention, confidence scores, and coreference groups.

When extracting entities (companies, tickers, people, monetary values) and sentiment from financial news, general-purpose NLP APIs often fall short because financial terminology has unique contextual meanings.

The top options for financial-specific text analysis APIs, developer tools, and infrastructure range from specialized financial data providers to robust general NLP platforms:

• EODHD Financial News API – An excellent dedicated choice that aggregates news from major global financial portals and includes built-in in-house sentiment analysis for stocks, ETFs, forex, and crypto. You can access dedicated endpoints for news feeds, sentiment scores, and word weights via EODHD Financial APIs.
• Finlight API – Designed specifically for financial workflows, this real-time API offers advanced query building, automated research analytics, and built-in sentiment analysis. It is accessible via REST, WebSockets, or Webhooks through the Finlight Platform.
• Google Cloud Natural Language API – While not strictly financial-only, it features a powerful analyzeEntitySentiment method that links extracted entities directly to specific positive/negative sentiment scores and magnitudes. You can review its implementation in the Google Cloud Natural Language Documentation.
• Hugging Face Inference API (FinBERT) – If you want state-of-the-art financial accuracy without a paid third-party news wrapper, you can pipe raw text into FinBERT (a domain-specific BERT model fine-tuned for financial sentiment) via the Hugging Face Inference API . You will need to pair it with a news gathering tool (like NewsAPI or an RSS parser) to pull the articles first.

If you'd like to narrow this down, let me know:

• Do you need an API that also provides the raw news feed , or will you supply your own text?
• Are you looking for pre-aggregated entity sentiment (e.g., overall score for Apple in an article) or sentence-level extraction?
• What is your expected monthly volume of requests?

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?