Amazon Comprehend

Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document. Use Amazon Comprehend to create new products based on understanding the structure of documents. For example, using Amazon Comprehend you can search social networking feeds for mentions of products or scan an entire document repository for key phrases.

You can access Amazon Comprehend document analysis capabilities using the Amazon Comprehend console or using the Amazon Comprehend APIs. You can run real-time analysis for small workloads or you can start asynchronous analysis jobs for large document sets. You can use the pre-trained models that Amazon Comprehend provides, or you can train your own custom models for classification and entity recognition.

All of the Amazon Comprehend features accept UTF-8 text documents as the input. In addition, custom classification and custom entity recognition accept image files, PDF files, and Word files as input.

Amazon Comprehend can examine and analyze documents in a variety of languages, depending on the specific feature. For more information, see

Languages supported in Amazon Comprehend⁠

. Amazon Comprehend's

Dominant language⁠

capability can examine documents and determine the dominant language for a far wider selection of languages.

Amazon Comprehend insights

Amazon Comprehend uses a pre-trained model to examine and analyze a document or set of documents to gather insights about it. This model is continuously trained on a large body of text so that there is no need for you to provide training data.

Amazon Comprehend analyzes the following types of insights:

Entities – References to the names of people, places, items, and locations contained in a document.

Key phrases – Phrases that appear in a document. For example, a document about a basketball game might return the names of the teams, the name of the venue, and the final score.

Personally Identifiable Information (PII) – Personal data that can identify an individual, such as an address, bank account number, or phone number.

Language – The dominant language of a document.

Sentiment – The dominant sentiment of a document, which can be positive, neutral, negative, or mixed.

Targeted sentiment – The sentiments associated with specific entities in a document. The sentiment for each entity occurrence can be positive, negative, neutral or mixed.

Syntax – The parts of speech for each word in the document.

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