Sentiment Analysis

Sentiment Analysis is the computational process of identifying positive, negative or neutral emotions in text. In HR, Sentiment Analysis helps interpret employee feedback from surveys, chats and reviews to reveal workforce mood and trends.

What is Sentiment Analysis

Sentiment Analysis uses natural language processing to classify text by emotion or attitude. It converts qualitative feedback into measurable scores so HR teams can track changes over time.

How does it work

Algorithms scan comments from surveys, emails and internal chat. They detect words and phrases that signal sentiment and assign values such as positive, negative or neutral. Aggregated scores highlight trends and hot spots.

Practical usage in HR

Organizations use Sentiment Analysis to monitor engagement, spot wellbeing issues, improve onboarding and evaluate policy changes. It supports evidence based decisions while reducing manual review of large comment sets.

Examples and scenarios

• Pulse survey comments reveal rising negative sentiment after a policy change.
• Exit interview text shows themes linked to retention risks.
• Internal chat analysis flags wellbeing concerns in a specific team.

Related HR concepts

Sentiment Analysis is closely related to employee engagement, people analytics, text analytics, natural language processing and predictive analytics. These terms support the interpretation and actioning of employee feedback.