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

Finding a text analysis API that extracts both entities and sentiment specifically tuned for financial news depends heavily on your budget, scale, and technical stack. Financial text is unique because general sentiment tools often misinterpret industry jargon (e.g., a "cut" in guidance vs. a physical cut).

Top API options range from developer-friendly plug-and-play services to enterprise-grade terminals:

• 1. Alpha Vantage News Sentiment API
  • Best for: Developers and algorithmic traders looking for a cost-effective, ready-to-use feed.
  • Features: It delivers live and historical market news paired with pre-calculated relevance scores and sentiment polarity scores mapped directly to specific equities, cryptocurrencies, or forex tickers mentioned in the text.
  • Access: Check out documentation and pricing on the Alpha Vantage Official Website.alphavantage.co
• 1. Google Cloud Natural Language API (Entity Sentiment Analysis)
  • Best for: Custom general-to-financial pipelines where you want granular entity-level sentiment.
  • Features: Unlike standard sentiment analysis that scores an entire article, the Google Cloud Natural Language API performs Named Entity Recognition (NER) and evaluates the specific sentiment (polarity and magnitude) attached to each distinct entity in the text. While not natively domain-trained on finance, it handles multi-entity disambiguation very well.
• 1. LSEG (Refinitiv) News Analytics & Open Calais
  • Best for: Institutional-grade finance applications and high-frequency trading inputs.
  • Features: Refinitiv's suite extracts rich financial metadata, ticker symbols, and real-time sentiment streams. It evaluates tone specifically through a financial lens, tracking how a single news piece impacts multiple related supply-chain or competitor entities.
  • Access: Explore enterprise capabilities via the LSEG Developer Community.community.developers.lseg.com
• 1. Custom LLM Stack (OpenAI / Anthropic / Llama via API)
  • Best for: Maximum flexibility and complex multi-dimensional extraction.
  • Features: Using structured outputs via APIs (like OpenAI's GPT-4o or an open-source model like FinBERT/Llama hosted on a serverless endpoint) allows you to define a custom JSON schema. You can prompt the model to extract every company, ticker, financial metric, and assign independent sentiment scores to each.github.com+1

To help narrow down the best choice, tell me:

• Are you looking for a pre-baked feed of financial news with sentiment already attached , or do you need an API where you pass in your own scraped text?
• What is your expected volume of requests per day/month?

For financial news, I’d shortlist these APIs:

API	Entities	Sentiment	Financial specialization	Best for
Google Cloud Natural Language	✅	✅ Entity-level + document	General-purpose	Easiest starting point
Azure AI Language	✅	✅	General-purpose	Microsoft/Azure stacks
Amazon Comprehend	✅	✅ Entity sentiment	General-purpose + custom entities	AWS pipelines
Financial-specific NLP model/API	Usually stronger for tickers, companies, events	Usually financial sentiment	✅	Trading/research applications

My first choice: Google Cloud Natural Language

Google's API is particularly convenient because its Entity Sentiment Analysis combines entity extraction with sentiment toward each entity. For example, given:

“Tesla shares jumped after the company reported stronger-than-expected earnings.” you can get an entity such as Tesla plus sentiment associated with that entity, rather than merely getting a sentiment score for the entire article. Google exposes this through analyzeEntitySentiment.Cloud Documentation+1

cloud.google.com

Azure is another strong general-purpose option

Microsoft's Azure AI Language API supports both entity recognition and sentiment analysis, including sentence-level sentiment, confidence scores, and opinion targets.Microsoft Learn

learn.microsoft.com

AWS

Amazon Web Services Comprehend provides entity recognition and sentiment analysis, including sentiment associated with particular entities. It also supports custom entity recognition, which can be useful if you want to detect financial concepts that aren't covered well by generic NER.Amazon Web Services, Inc.

aws.amazon.com

One important caveat: if your goal is financial research or trading, generic NLP APIs may not be sufficient. You may want entities such as ticker symbols, companies, executives, financial instruments, sectors, earnings events, guidance changes, upgrades/downgrades, and M&A, plus sentiment specifically calibrated to financial language. A sentence like “the company missed estimates but maintained guidance” is much more nuanced financially than ordinary positive/negative sentiment.

If you tell me your expected article volume (e.g. 10K, 1M, or 100M articles/month) and whether you need real-time processing, I can narrow this to 3–5 APIs and compare pricing, latency, entity types, financial sentiment quality, and API examples.

When looking for a text analysis API that extracts entities (Named Entity Recognition - NER) and sentiment specifically tailored for financial news , your options generally fall into three categories: dedicated financial data APIs with built-in NLP, generalized AI/NLP cloud services, or DIY developer pipelines using financial language models.

Top options available on the market categorized by their approach:

1. Dedicated Financial APIs with Built-in Sentiment & Entity Data

These services pull live financial news, automatically map them to specific entities (like tickers, companies, and executives), and pre-calculate sentiment scores.

• Alpha Vantage News Sentiment API : Provides live and historical market news mapped directly to relevant tickers, cryptocurrencies, or forex pairs. It returns a relevance score and a specialized sentiment score/label for each mentioned entity.
• Financial Modeling Prep (FMP) Stock News API : Offers structured stock market news feeds complete with ticker tagging and pre-calculated sentiment scores, making it straightforward to aggregate sentiment at the company level.site.financialmodelingprep.com+3
• Bloomberg Event-Driven Feeds : The industry standard for institutional quants. It delivers real-time machine-readable news, deep proprietary entity tagging, novelty scores, and sentiment analytics directly from Bloomberg terminals and feeds.professional.bloomberg.com+2
• LSEG (Refinitiv) Data Platform : Another enterprise-grade alternative providing robust natural language processing (NLP) features, entity mapping, and real-time sentiment scoring across global financial news feeds.

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

If you already have a news ingestion pipeline (e.g., via RSS or Web scraping) and need an API strictly to process the text for entities and tone:

• Google Cloud Natural Language API : Performs robust Named Entity Recognition (NER) and sentiment analysis. While not natively fine-tuned for financial jargon, it handles general entity extraction and emotional tone well.
• Amazon Comprehend : Offers custom classification and native entity recognition. You can feed it financial text blocks to isolate organizations, dates, quantities, and general sentiment.

3. DIY Custom Pipeline: FinBERT via Hugging Face Inference API

Financial text carries intense nuance (e.g., words like headwinds, guidance , or downgrade shift meanings radically compared to casual English). Many developers bypass general APIs by combining a standard news scraper with a specialized financial model:site.financialmodelingprep.com+1

• Hugging Face Inference API : You can programmatically query specialized open-source financial models like FinBERT (trained exclusively on financial data to classify text as positive, negative, or neutral) without managing the heavy infrastructure locally.youtube.com+2

To help narrow down the best choice, tell me:

• Do you need an API that provides the news feed itself , or do you already have a source and just need to analyze custom text?
• What is your expected volume (requests per day) and budget?

When extracting both entities (like company names, tickers, executives) and sentiment specifically tailored to financial news, you generally choose between two categories of APIs: Dedicated Financial Market APIs (which pre-parse and tag financial entities/sentiment out of the box) or General Cloud NLP APIs (which perform custom entity sentiment analysis on raw text you feed them).apitube.io+2

Top Dedicated Financial News & Sentiment APIs

These providers ingest financial news feeds natively, extract relevant stock/crypto/forex tickers, and assign sentiment scores.eodhd.com+1

1. Alpha Vantage News Sentiment API
   • Best for: Out-of-the-box ticker-level sentiment and relevance scores.
   • What it does: Provides live and historical financial news articles coupled with pre-calculated relevance and sentiment scores (bullish/bearish/neutral labels with continuous numerical polarity) mapped directly to specific ticker symbols and topics.apitube.io+1
2. Finnhub Stock News Sentiment API
   • Best for: Real-time monitoring and institutional-grade alternative data.
   • What it does: Scans major financial news outlets, automatically tags companies/symbols, computes daily sentiment scores, and tracks buzz statistics compared to historical averages.qveris.ai+3
3. Marketaux Financial News API
   • Best for: Deep entity extraction (entities, sentiments, and high-precision stock matching).
   • What it does: Offers granular JSON payloads mapping entities (companies, crypto, people) to specific sentiment scores per article, allowing complex filtering by entity and sentiment thresholds.apitube.io+2
4. EODHD Financial News Feed & Sentiment API
   • Best for: Global market coverage across stocks, ETFs, forex, and crypto.
   • What it does: Continuously processes global financial portals, attaching word weights and polarity sentiment metrics to ticker symbols.eodhd.com+1

General Cloud NLP APIs (Custom Text Processing)

If you are already scraping your own custom feeds and need an API to process raw text strings rather than using a pre-packaged news database:

1. Google Cloud Natural Language API
   • Best for: Advanced linguistic entity-sentiment mapping.
   • What it does: Offers an analyzeEntitySentiment endpoint which extracts distinct entities (people, organizations, locations) and evaluates what specific sentiment (positive/negative score and magnitude) is tied directly to each mention of an entity within the text.docs.cloud.google.com+1

To help narrow down the best choice, could you tell me:

• Do you need an API that provides the news feed itself pre-tagged, or are you passing your own scraped text into an analyzer?
• What asset classes (US equities, crypto, forex) are you focusing on?

I can recommend the most cost-effective tier or provide a quick integration example.