Azure Automation: Analyzing Twitter sentiments using Azure Logic Apps

Azure Automation: Analyzing Twitter sentiments using Azure Logic Apps

This article configures Azure Automation for Analyzing Twitter sentiments using Azure Logic Apps and stores them into Azure SQL Database.

This article configures Azure Automation for Analyzing Twitter sentiments using Azure Logic Apps and stores them into Azure SQL Database.


As a technical blogger, I want to analyze the tweets for a specific hashtag, perform their sentiment analysis, and store them into Azure SQL Database to prepare visualization or further analysis. It is not feasible to store the Tweets manually in a database. Azure Logic Apps is an excellent tool for analyzing data for social platforms such as Twitter, LinkedIn, Facebook, etc. It allows automating design workflows using triggers, connectors.

Twitter is a social platform with allows businesses to reach a broad audience without any intermediaries. The sentiment analysis is a crucial element of social media networking, and it monitors emotions on social content. It involves the identification and classification of text such as positive_, _negative,orneutral.

For example, in the following screenshot, you can see a tweet sentiment as positive. Reference: MonekyLearn

In the previous articles, we explored various practical usage of Azure Logic Apps. You can refer to those in TOC. This article introduces new tweets from the Twitter application and imports them into the SQL database with their sentiment score.

Create a logic app and connect it with a Twitter connector

We can use the Twitter connector for tasks such as post tweets, get tweets from the timeline, get a list of your followers. You can look tweets for a specific keyword or Text. Once the azure logic app detects a new tweet, it initiates a workflow based on your configuration.

Navigate to the Azure portal, go to logic apps and create a new logic app. Enter the resource group, logic app name along with the azure region.

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