- Harness the power of employee sentiment using Deep learning and Natural Processing Language
- Sentiment analysis uses natural language processing (NLP) to gain an unbiased insight on how your employees are genuinely feeling.
- Sentiment analysis involves classifying opinions in text into categories like “positive” or “negative” often with an implicit category of “neutral”.
- Powerful machine learning algorithms automatically organize and assign sentiment scores to text feedback.
- The quantification of sentiment can be used to make data-driven decisions about which projects priorities will have the biggest impact, and for whom.
- The AI-powered sentiment analysis engine can scans each interview to provide deep insights on candidate attitude, positivity and overall sentiment – crucial for positions that require more than
- When the landscape of what is driving dissatisfaction is made visible through machine learning and sentiment analytics, it becomes easier to turn confusing negative or neutral score into strategic wins.
- Sentiment analysis, surveys can be seen as the “voice of the employee.” Engagement surveys that are distributed on a regular basis show engagement scores.