Amazon Comprehend Documentation

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection features so you can integrate natural language processing into your applications. 

Keyphrase Extraction

The Keyphrase Extraction API returns key phrases or talking points and a confidence score to support that this is a key phrase.

Sentiment Analysis

The Sentiment Analysis API returns an overall sentiment score of a text (Positive, Negative, Neutral, or Mixed).

Syntax Analysis

The Amazon Comprehend Syntax API enables customers to analyze text using tokenization and Parts of Speech (PoS), and identify word boundaries and labels like nouns and adjectives within the text.

Entity Recognition

The Entity Recognition API returns named entities ("People," "Places," "Locations," etc.) that are categorized based on the provided text.

Custom Entities

Custom Entities allows you to customize Amazon Comprehend to identify terms that are specific to your domain. Using AutoML, Comprehend will learn from a small private index of examples (for example, a list of policy numbers and text in which they are used), and then train a private, custom model to recognize these terms in any other block of text. 

Language Detection

The Language Detection API identifies text written in over 100 languages and returns the dominant language with a confidence score to support that a language is dominant.

Custom Classification

The Custom Classification API enables you to build custom text classification models using your business-specific labels without learning ML. For example, your customer support organization can use Custom Classification to categorize inbound requests by problem type based on how the customer has described the issue. Creating a custom model is simple. You provide examples of text for each of the labels you want to use, and Comprehend trains on those to create your custom model.

Topic Modeling

Topic Modeling identifies relevant terms or topics from a collection of documents. It can also help identify the most common topics in the collection and organize them in groups, and then map which documents belong to which topic.

Comprehend Medical

Medical Named Entity and Relationship Extraction (NERe)

The Medical NERe API returns medical information such as medication, medical condition, test, treatment and procedures (TTP), anatomy, and Protected Health Information (PHI). It also can help identify relationships between extracted sub-types associated to Medications and TTP. There is also contextual information provided as entity “traits” (negation, or if a diagnosis is a sign or symptom).

Medical Ontology Linking

The Medical Ontology Linking APIs can identify medical information and link the information to codes and concepts in standard medical ontologies. The Medical Ontology Linking APIs can also detect contextual information as entity traits (e.g. negation).

Additional Information

For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at, or other agreement between you and AWS governing your use of AWS’s services.