Problem identification & grouping for problem reports, improvement ideas and employee feedback comments.

Peopelgeist's employee comment analysis is the cutting-edge solution to perform topic grouping and sentiment analysis on your employee comments to get actionable insight.

The only text-analysis solution specialized for operational topics & employee concerns, focusing on concrete workplace-problems teams can act on.


Why peoplegeist's employee feedback & review analysis

1. Sentiment Analysis 2.0: Triple play

Word clouds and traditional sentiment-analysis will show you prominent words like "boss" or "work". They leave you guessing what about "boss". Is boss "nice", is boss "loud", is boss "helpful"? It's anybody's guess.

Example of triple extraction:

"Boss is always late for meetings" [NEGATIVE]

  • Topic: What it is about? i.e., "boss"
  • Opinion: What about it? - "is always late"
  • Sentiment: Positive/Negative

Extracting and showing the full triplet provides
actionable insight: Boss - always late

2. Multi-language for diverse workforce

Your workforce is global and diverse. Our solution is multi-lingual and allows your staff to express themselves in their mother tongue.

If you have a:

  • multi-national setup,
  • specialists hired from around the world,
  • or a mixed team with diverse backgrounds

You should be able to understand your employees' concerns, no matter where they are from.

Contact us to ask for a specific language.

3. Concrete workplace topics

Our solution was made for operations and people topics.

Generic off-the-shelf sentiment-analysis solutions are general purpose analyzers. You can get the general gist.

Peoplegeist's solution was trained for workplace related terms and focuses on concrete day-to-day problems.

Use it for example for:

  • Shop floor incidents and production problems.
  • Continuous improvement problem collection.
  • Kaizen incident reporting.

The more concrete and specific a problem, the easier to act on it.

Start gaining actionable insights from your employee comments

Again, we had barely time for our breaks today. There were so many packages and so few people around. Teamwork is really great, and everyone stayed to finish the day but the unplanned overtime is hard for family life.

In the summer the Hall B gets really hot. For us older guys, you need more breaks in between. To finish the job, I had to do unplanned overtime again.

Automatic topic grouping

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Sentiment analysis 2.0 for employee feedback comments

The most valuable information in a survey is often in the comments. The comments help managers understand what employees think.

Reading through all the comments is very time intensive. Sentiment analysis is a technique in natural language processing (NLP) that uses machine learning and other techniques to automatically grasp the overall mood of comment.

This technique is used heavily in customer review analysis to quickly extract the topic of a product review and if the review was positive or negative.

Classic sentiment analysis can automatically extract the topic and the sentiment (positive/negative) of an employee comment. It helps you to detect and categorize hundreds of employee feedbacks by topic.

Sentiment Analysis 2.0 is what we call the next generation of text-analysis. For truly actionable insight you need not only the topic but also the prevailing problem.

Detecting that a comment is about “the boss” is good. Knowing exactly the opinion “boss helpful” is what makes the analysis helpful.