Amazon Comprehend for Text and Document Analysis

Sajjad RahmanSajjad Rahman
3 min read

AWS provides several AI and ML services. We start with the Text and Document analysis services, particularly Amazon Comprehend, an AWS natural language processing (NLP) service that makes it easy to analyze text and documents.

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image: AI/ML services [1]

Amazon Comprehend is a natural language processing (NLP) service on AWS, which makes it easy to analyze text and documents. Now start here

What is Amazon Comprehend?

Using advanced NLP techniques, Amazon Comprehend analyses the content of documents and text. This service can help us extract meaningful insights, recognize key information, detect sentiment, and even identify specific entities (like people, phone numbers, or locations) from the data.

Key Features of Amazon Comprehend

Amazon Comprehend performs well in unstructured data. Take a bird's eye view of what it does

  1. Entities Detection: Identify important entities like names, dates, locations, and events. This feature is great for summarizing key details in large documents.

  2. Key Phrases: It can identify groups of words that represent key concepts in the text. For example, a news article about sports might highlight team names, game locations, and scores.

  3. Personally Identifiable Information (PII) Identification and Redaction: This feature detects sensitive data, like names, dates of birth, and addresses.

  4. Language Detection: Quickly identify the primary language of a document, helping understand multilingual datasets at a glance.

  5. Sentiment Analysis: Analyze text to determine if it has a positive, negative, or mixed tone. Which is more useful for understanding the customer feedback.

  6. Syntax Analysis: Break down sentences to identify parts of speech, like nouns, verbs, and adjectives.

Additional features include:

  • Event Detection: Recognize specific events and details in a document.

  • Targeted Sentiment: Focus on sentiment related to particular entities mentioned.

Customization with Amazon Comprehend

We can use Amazon Comprehend’s pre-trained models or create custom ones with our own labeled data. Custom models are available for:

  • Custom Document Classification: Build categories based on unique needs.

  • Custom Entity Detection: Recognize specific terms or phrases.

  • Document Topic Modeling: Group documents useful for organizing large collections of text.

What is more, Comprehend Medical is a specialized version pre-trained with medical terminology for analyzing healthcare data.

How to Access Amazon Comprehend

Accessing Amazon Comprehend is straightforward:

  1. AWS Console: Use the web interface to test and run analyses.

  2. Comprehend APIs: Integrate with an application for a customized experience.

On the other hand, you can run real-time analysis for quick insights or asynchronous analysis for large files. Comprehend supports UTF-8 text and can process image, PDF, and Word files for classification or entity recognition.

Getting Started with Custom Models

Though it updates its training data every day, we can run a custom one based on our specification

  • Custom Classification: Create categories that match our task , but we have to provide the labelled data, and Comprehend and train a model to classify new documents.

  • Custom Entity Recognition: Define specific terms or phrases that are recognized

Comprehend also supports Flywheels, which allow us to update and maintain custom models for continued performance.

Benefits of Using Amazon Comprehend

Amazon Comprehend’s continuous updates mean you don’t always need to provide training data, as the models improve automatically.

  • Analyze documents in real-time.

  • Process large files asynchronously.

  • Run one-step document processing for custom classification and entity recognition.

Ready to get started? Explore Amazon Comprehend on the AWS Console today and begin your journey into text analysis!

reference : https://aws.amazon.com/comprehend/

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Written by

Sajjad Rahman
Sajjad Rahman

As a Flutter developer, I am constantly learning and trying to implement new concepts into my projects. In addition, I devote time to studying Machine Learning. I have a passion for contributing to open-source projects.