Case Study

Sentiment Analysis Solution For Employee Engagement Platform

A sentiment analysis solution for an employee engagement platform utilizes natural language processing techniques to analyze and understand the sentiment expressed in employee feedback, surveys, and communication.

At a Glance

By accurately identifying positive, negative, or neutral sentiment, the platform can provide valuable insights to employers, helping them gauge employee satisfaction, identify areas of improvement, and enhance overall employee engagement. Sentiment analysis allows organizations to capture real-time feedback from employees, whether through surveys, social media channels, or other communication channels. By analyzing the sentiment behind these responses, organizations can gain actionable insights into the overall mood and sentiment of their workforce, enabling them to promptly address any concerns or issues.

Key Highlights

70% reduction in turnaround time

50% increase in productivity

Improved Document Management

Document Management

Streamlined Communication

Enhance Customer Satisfaction

Challenges

Develop custom solution as there are many players in the market but not offering advance insights.
Gauge & evaluate employee sentiments from 50,000+ emails & lakhs of real-time entries/records on their platform.
Evaluate each comments/emails/records to identify and categorize opinions expressed in a piece of text.
Base on sentiment of each opinion; solution decide rating itself.

About Client

A Canadian company that provides SaaS based employee recognition solutions to boost employee engagement in modern enterprises.

The company is working in this domain for more than 100 years, having offered employee recognition solutions to global organizations with a workforce between 10000 to 85000 employees.

HRTECH

Industry

ENGAGEMENT

Domain

CANADA

Location

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How we helped?

The solution provider was looking to revolutionize their employee recognition process with advance intelligence. With the same vision in mind, the client collaborated with Azilen to develop an intelligent sentiment analysis solution for its proprietary employee engagement platform.

The Solution

To develop a powerful solution, it was important to understand the employee recognition scenario, devise a plan to transmit the crude information into valuable data and deliver the final results after sentiment analysis. The solution passed through the following stages :

Scenario Analysis

Managers wrote recognition messages for team members periodically to laud their achievements and performance.
Employees provide feedback on manager role through regular surveys.
The information is recorded in English language and is available through API.

Development of Data Integration Service (DIS)

Transmits crude data (information) accessed via an inbound web API.
Extraction, cleaning and organization of crude data.
Transmission of organized data to prompt ‘sentiment analyzer’.
Publishing the final results of sentiment analyzer through outbound API export.

Sentiment Analyzer (SA)

Activated on trigger generated via DIS to prompt sentiment analyzer.
Process the available data using NLP to identify, analyze and categorize positive or negative sentiments.
Analyze employee survey ratings.
Establish co-relation between the NLP-based sentiment analysis and survey ratings to come up with consolidated rankings.
Corroborating dataset rankings with positive/negative sentiment percentage.
Transmitting the final rankings back to the DIS.

Sentiment Analysis Solution For employee Engagement Platform

Employee engagement is a crucial factor for organizations to drive productivity and create a positive workplace culture. By integrating sentiment analysis into employee engagement platforms, organizations can gain valuable insights into the emotions, opinions, and satisfaction levels of their workforce.

This data-driven approach enables proactive measures to address concerns, boost morale, and create a more engaging work environment, ultimately leading to increased employee satisfaction and retention.