Example of integrating & using Amazon Textract, Amazon Comprehend, Amazon Comprehend Medical, Amazon Kendra to automate the processing of documents for use cases such as enterprise search and discovery, control and compliance, and general business process workflow.

Open innovation with 60 minute cloud experiments on AWS

⛳️ PASS: Amazon Web Services Certified (AWS Certified) Machine Learning Specialty (MLS-C01) by learning based on our Questions & Answers (Q&A) Practice Tests Exams.

A boilerplate solution for processing image and PDF documents for regulated industries, with lineage and pipeline operations metadata services.

An inclusive chat that avoids negative messages and translates the content in the language that you choose, tracking the main topics of a chat room.

Automatically generate multi-language subtitles using AWS AI/ML services. Machine generated subtitles can be edited to improve accuracy and downstream tracks will automatically be regenerated based on the edits. Built on Media Insights Engine (https://github.com/awslabs/aws-media-insights-engine)

How to train a custom NLP classifier with AWS Comprehend?

QuickSeek is a chrome extension that allows you to easily search and navigate through a YouTube video, you can quickly find and watch only parts of the video that contain words you are looking for. The Chrome extension uses Amazon Transcribe to make the audio searchable and Amazon Comprehend to perform sentiment analysis on the transcript.

A web-app that aggregates a feed of positive news.

This repository contains a series of 4 jupyter notebooks demonstrating how AWS AI Services like Amazon Rekognition, Amazon Transcribe and Amazon Comprehend can help you extract valuable metadata from your video assets and store that information in a Graph database like Amazon Neptune for maximum query performance and flexibility.

Get additional insights from your data by running it through Amazon Comprehend

A sample guide to building a serverless document processing application that can make intelligent flow-control decisions after classifying the input document type.

Elasticsearch ingest processors using Amazon Comprehend for NLP analysis

This demo processes conversations in real-time with the Amazon Comprehend natural language processing (NLP) service to gain insights about what was said.

Users can send a text string to the API and receive its sentiment result (POSITIVE, NEGATIVE, NEUTRAL, MIXED).

As an Intelligent Process Automation showcase. We combined Business Process Management with Artificial Intelligence, and the result was awesome! Find out more: https://www.novatec-gmbh.de/en/blog/ipa-camunda-comprehend/

Use AWS Comprehend and GCP to do sentiment Analysis on Reviews for Comic series South Park and Rick&Morty.

Sample audio transcription and analysis pipeline using Amazon Transcribe, Amazon Comprehend.

Translate Slack message with flag emoji(e.g. :jp: :us: :uk:) reaction. :smile: Use only AWS Products(AWS Lambda functions in Go, Amazon Comprehend, Amazon Translate)

Dataiku DSS plugin to use the Amazon Comprehend Medical API 🩺

This construct creates the foundation for developers to explore the combination of Amazon S3 Object Lambda and Amazon Comprehend for PII scenarios and it is designed with flexibility, i.e, the developers could tweak arguments via CDK to see how AWS services work and behave.

Amazon Comprehend sample code that calls Sentiment and Key phrase APIs and does additional processing.

Drupal 11 custom module that uses Amazon Comprehend to automatically classify content and assign taxonomy terms using AI. Built with Drupal best practices and AWS integration.

Front-end website | Backend API | Authentication | Backend compute functions | Asynchronous reporting workflow | Distributed tracing | Monitoring features | Improving performance

Dataiku DSS plugin to use the Amazon Comprehend APIs 📚

Program to analyze tweet sentiment

Example php scripts making sigv4 signed aws api requests

NLP pipeline (AWS/Local): sentiment, key phrases, NER + optional translation with exportable outputs.

Provides a reliable political feed for readers to become knowledgeable and informed voters on the state political level.

In this module you will learn how to analyze topic modeling output from Amazon Comprehend, then perform topic modeling on two documents with a known topic structure.