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Role Of Text Analytics Service As A Connector In Azure Logic Apps

13 minute read

Introduction

In this article, we will learn, via a demo application, about the role of Text Analytics Service, a Cognitive Services offering, as a connector in Azure Logic Apps.

Prerequisite

Overview

Azure Logic App is an Azure offering for automating business workflow by integrated architecture. This integration can be between two cloud services or between on-premise & Cloud, or between two on-premise services.

Microsoft has provided a large number of managed services known as Connectors to play role in business workflow as required. This connector acts as Trigger to initiate a workflow and responds to triggers by executing or implementing certain functionality on the data received in the request.

Cognitive Services, in short, helps to build intelligent apps and bots, enabling them with the power of talking, speaking, hearing, predicting, etc. This offering can be leveraged into Azure Logic App, as a connector to work upon any given data in a workflow.

In this article, we will learn about Cognitive offering Text Analytics. Text Analytics API in Azure Logic Apps seamlessly evaluates sentiment and topics to understand a user’s commands. The API, as described by Microsoft, is designed to detect sentiment, key phrases, topics, and language from your text. It is a suite of text analytics web services built with Azure Machine Learning. No training data is needed to use this API; just bring your text data. This API uses advanced natural language processing techniques to deliver best in class predictions.

For pricing, provisioned regions, and other key details, read Getting Started with Microsoft Azure Cognitive Services — Text Analytics API.

To understand in a better way, let me describe workflow scenarios.

Cognitive Text Analytics API – Detect Sentients

In this demo, when a user tweets with configured Twitter Hashtag, Logic App gets triggered and passed to Cognitive Text Analytics Connector for detecting the sentiments of the tweet (text).

If the tweeted text is found to be harsh or with bad or abusive language, it is sent via mail to Gmail account for reviewing purpose. The Mail is sent using Outlook.com connector. If the tweeted text passes the sentiment analysis with the desired score, it’s, then, saved to Azure Blob Storage for further processing.

Cognitive Text Analytics API – Extract Key Phrase

In this demo, we will build an Azure Logic App for managing tweets for capturing key phrases using Twitter Connector as Trigger, and Cognitive Text Analytic and Storage Blob connector as Actions.

When a user tweets with configured Hashtag, Logic App gets triggered and passed to Cognitive Text Analytics Connector for extracting the key phrase from the Tweet (text).

Cognitive Text Analytics API – Detect Language

This demo would create an Azure Logic App for managing tweets with different languages & saving it to Azure Blob storage according to the language, all this in less than 10 mins. We will be using Twitter connector as Trigger, Text Analytics Cognitive service & Azure Blob Storage connector as Action.

Summary

Hope this article will help you to explore the use of Cognitive Text Analytics API in building core workflows using Azure Logic App. At the time of writing this article, all three connectors demonstrated above are in preview. I ould highly recommend you follow the steps shown in the demo video, and build up your own workflows with more interesting scenarios. Do share your experience with me in the below comments. Don’t forget to subscribe to my YouTube Channel for more such interesting videos.

Happy Azure Coding, Thank You!

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