Average Issue Time Spent In Emotions

The chart helps you analyze which emotions are your teams' expressing at the issue level? And for how long are these emotions expressed?

The chart can be used to detect issues with positive or negative emotions and consider questions like:

  • Do the team members express negative opinions? Which issues, in particular, are affected by negative emotions?

  • Can this information be used to help teams' collaborate better?

  • Is there a strong correlation between the teams' emotions and their productivity?

Check out our recent case study with Atlassian to analyze the impact of emotions on teams' productivity.

This chart helps you identify how your team is communicating. A bigger blob of negative emotions is typically a red flag. You can click on any emotion blob and it will take you to the list of issues which have that emotion. This can help you to immediately identify issues which require your attention.

How we classify comments into emotions ?

Our emotion classifier is trained on huge datasets of conversations labeled with emotions. using those machine learning models we predict emotions based on comments on an issue.

The latest conversations emotion is considered as the most relevant emotion for that issue.

The Comment below is classified as anger (The intensity of anger may vary). It is possible that a comment is classified as anger but the intensity is very less and thus based on the readers perspective reader may perceive that it is not anger

Emotion examples

  • "All the must-haves are checked off " - Joy

  • "This is an interesting idea, but it's not the first behavior I'd expect when clicking on any given metric visualization, so it would need to be implemented carefully." - Anticipation

  • "Isn't it fixed in the last release" - Surprise

  • "I'm happy to reopen this.I originally closed because value_type is undocumented.

    We should probably document it (and make sure it works in all cases)" - Trust

  • "I am against modifying the startup scripts, that's entering territory that we do not support" - Anger

  • "Sorry for the noise, you're right.It was a problem with APT caching being done in my infrastructure." - Sadness

  • "I've done more testing on this, which uncovered two smaller bugs" - Disgust

  • "This is not the right fix. this may break in some scenarios" - Fear

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