Amazon Comprehend

  • A managed Natural Language Processing (NLP) service that you can use to extract meaningful information from unstructured texts so you can analyze them in a human-like context.
  • It is an off-the-shelf solution that does not require deep machine learning expertise to get started.
  • Works with social media feeds, web pages, comments, product reviews, articles, or emails.
  • Can analyze texts in real-time by using built-in and custom models.

Common Use Cases

  • Sentiment analysis for social media posts
  • Organize documents by topics
  • Knowledge management and discovery
  • Classifies support tickets for better issue handling
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  • Medical cohort analysis

Amazon Comprehend generates insights in six (6) categories:

  • Entities
    • Detects and categorizes real-world objects like date, organization, person, quantity, brands, or even a title given to a song or movie.
    • Custom Entity Recognition 
      • Allows you to identify new entities that are not supported by the preset entities. 
      • This is useful if you want to extract entities that are specific only to your business, such as product codes.
  • Sentiment
    • Detects and classifies emotions into neutral, positive, negative, or mixed.
  • Language
    • Detects the language used in a text by using identifiers from RFC 5646. 
    • Useful for multilingual companies or applications.
  • Key Phrases
    • A key phrase refers to a noun or a noun phrase that describes a particular thing.
  • Personally Identifiable Information (PII)
    • Determines sensitive information that could be used to identify a person, such as full name, birth date, bank account number, phone number, or email.
  • Syntax
    • Determine the different parts of speech used in the document, such as noun, pronoun, verb, adjective, adverb, etc.


  • Each insight is associated with a confidence score.
  • A confidence score is between 0 and 100, indicating the probability that a given prediction is correct.
  • A product review with a positive sentiment and a 0.99 confidence score highly suggest positive feedback from a customer.
  • Topic Modeling
    • Classifies a collection of documents according to its common subject.
    • For example, you can use Topic Modeling to categorize news articles into politics, sports, business, entertainment, etc. 
  • Comprehend custom
    • It helps non-experts in machine learning build and train their own NLP models suited to their specific needs.
    • Amazon Comprehend uses a machine learning method called transfer learning to train custom models.


  • Charges are based on units where a single unit is equal to 100 characters. 
  • 3 unit (300 characters) minimum charge per request.
  • All insights except for Syntax analysis are charged for $0.0001 per 10M units. Syntax Analysis is charged for $0.00005 per 10M units.
  • Topic Modeling has a flat rate of $1.00 per job.


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