Sentiment analysis | AI for open comment employee feedback: The rewards, the risks, and the reality

For every stride forward HR has made in its ability to listen to its employees, there is still an inescapable frustration from workers that their voices are not heard.

In a LeadershipIQ study of over 27,000 executives, employees, and managers, less than a quarter of employees say their leadership always encourages and recognizes feedback or suggestions for improvement. Indeed, whilst we have moved beyond the annual engagement survey of old to more sophisticated forms of employee listening, frustrations over compensation, recognition, wellbeing, flexibility, and other aspects of the employee experience are ever-present.

AI has already played a significant role in advancing capabilities for analyzing employee feedback. AI-driven algorithms allow HR teams to rapidly process huge data sets of employee feedback data, saving employee listening professionals from slaving over Excel spreadsheets or survey platforms. Every employee listening tool worth its salt will now offer some type of tool that uses AI to visualize trends in such data and even provide recommendations.

Processing qualitative employee feedback through open-text comments, however, is a different story. Open comments allow employees to share their opinions unshackled from Likert scales or forced ranking surveys. This freedom creates an outstanding opportunity to truly get to grips with employee sentiment, feedback, and even specific recommendations – but also the challenge of analyzing and drawing actions from hundreds, thousands, or tens of thousands of employees.