Employee Review Sentiment Analysis with Azure NLP 👩🏻‍💻👨🏻‍💻🤷🏻‍♀️

Hi Everyone! 😊👐🏻, I am back with another interesting content 😀today we will learn how to “Perform Employee Review Sentiment Analysis Using NLP in Azure ML: A Step-by-Step Guide” .

In today’s data-driven world, understanding employee feedback is crucial for improving workplace culture and boosting productivity. With the power of Natural Language Processing (NLP) and Azure Machine Learning (Azure ML), you can transform raw employee reviews into actionable insights. This guide will walk you through performing sentiment analysis on employee reviews using Azure NLP.

Step 1: Go to you Azure portal. Search “Azure AI services” resource and then select “Language service”.

Step 2: Select create a Language service plan. You will be taken to a page to Select additional features. Keep the default selection and click Continue to create your resource.

Step 3 : On the page Create Language, configure it with the following settings:

• Subscription: Your Azure subscription.

• Resource group: Select or create a resource group with a unique name.

• Region: Select the closest geographical region. If in eastern US, use “East US 2”.

• Name: Enter a unique name.

• Pricing tier: Free F0 or S if Free F0 is not available

• By checking this box I acknowledge that I have read and understood all the terms below: Selected.

• Select Review + create then Create and wait for deployment to complete.

Step 4 : In another browser tab, open Language Studio at https://language.cognitive.azure.com and sign in. Fill all the default data which will be asked.

Then select “Classify text” and click on “Analyze sentiment and mine opinions”.

Step 5 : Finally , you have to give your own inputs whatever you want to give. Example, here I have given below data “Employee Performance Review”. Then select Run

Underwhelming performance and lack of initiative
Jane Smith, Sales Department
7/15/2024
Jane’s performance has been inconsistent and lacks the drive expected 
in her role.Her responses to team initiatives are often delayed, impacting 
overall productivity.Improvement in her engagement and punctuality is needed.

The analysis splits the review into individual sentences and provides sentiment insights for each one, based on the input data.

Similarly, Azure Language Studio offers a range of features tailored to various data requirements, enabling you to customize and enhance your analysis.

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