Peakon's semantic topics analyzes employee comments using Natural Language Processing (NLP) to identify important themes and overall sentiment.
Unlike Keyword topics for comments, semantic topics groups comments into themes based on their meaning.
- Predefined topic labels that are contextual, longer and more meaningful than keyword topics.
- Semantic topics span across multiple drivers and can form part of a wider theme.
- There's no required minimum number of comments.
- There's no required minimum number of enabled questions.
- Semantic topics generate in real time.
- Semantic topics use comments from all time.
- Currently only English language support is available.
- There's no third-party sub-processing or additional opt-ins involved. The semantic topics feature is entirely in-house.
How it works
Using a combination of NLP and machine learning, Peakon groups comments with similar meaning to form topics. These comment groups display under overall themes, helping users to identify potential areas of focus.
This requires Peakon to define the topics for classification upfront instead of topic themes automatically generating, as per Keyword topics for comments.
It's not possible to remove topic themes from semantic topics.
Current list of topic themes
- There are opportunities to learn
- There are no opportunities to learn
- Need more opportunities to learn
- Workload prevents development
- Workload feels too light
- Workload is unmanageable
- Workload is manageable
- Job description is clear
- Job description is unclear
- Job description doesn’t reflect the work
- Job description reflects the work
- Lack of recognition
- Lack of autonomy
- Signs of employee burnout
- Lack of support at work
- Lack of fairness