Twitter Stream Analysis with Azure Machine Learning

By for February 22, 2017

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Analyzing data streams in real-time is a problem that a lot of businesses can relate to. In this fast paced digital era, enterprises depend on making quick and intelligent decisions. This tutorial lets you stream data from Twitter using Key Words of your choice including #hashtags and @mentions.
> **Note:** If you have already deployed this solution, click [here]( to view your deployment. ### Estimated Provisioning Time: 15 Minutes [![Solution Diagram](]( This solution sets up the infrastructure in the diagram above. The various steps are as follows: * Setting up an Azure WebJob to collect Twitter data based on user specified keywords. * Pumping ingested tweets into Azure Event Hub which can accept millions of events per second. * Processing incoming tweets with an Azure Stream Analytics job that stores the raw data in Azure Blob Storage and Azure SQL Database. * The Stream Analytics job calls an Azure Machine Learning web service to determine the sentiment of each tweet. * Visualizing real-time metrics about inferred sentiment using Power BI. ## Prerequisites To run the TwitterClient web job, you will need: 1. A [Twitter account]( 2. A [Twitter application]( 3. Twitter's Streaming API OAuth credentials - On the Twitter application page, click on the *Keys and Access Tokens* tab - *Consumer Key (API Key)* and *Consumer Secret (API Secret)* can be found under **Application Settings** section - Under **Your Access Token** section, click on *Create my access token* to obtain both *Access Token* and *Access Token Secret* More details on Twitter's Streaming API OAuth access token can be found [here]( ## Disclaimer ©2017 Microsoft Corporation. All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third party data was used to generate the solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licenses in order to create similar datasets. ![ ](