Your Emotional Fingerprint on Slack

Update: Due to several requests, I’ve uploaded the code of the visualisation part of the Emotional Fingerprint to GitHub. Enjoy!
This article was originally posted on June 10, 2015

I’ve been doubting between calling it an emotional footprint or fingerprint. You leave a digital, emotional footprint behind on social networks, so that would’ve made sense. However, your emotional state, and the emotional traces you leave behind, must be quite unique… Fingerprint it is!

This article discusses the steps it took to come up with the final design presented above. For the technical details, if there is enough interest I’ll just throw it on Github and you can all help perfect this rough prototype.

Version 1

Sentiment analysis provides interesting insights into social network behaviour. Our team uses Slack as its main means of communication, the perfect testbed for exploring new and interesting ways of visualising the emotional state and distribution based on our active discussions on Slack.

Version 1. A green square is a positive post, a pink negative. Grey is neutral. Brightness indicates level of positivity.

With about 6 months of data, a week overview gives us interesting insights on both the distribution of activity across weekdays, but also the emotional nature of our posts. The above image shows a first attempt at visualising the sentiment analysis data, from Sunday (left) to Saturday (right).

The small bar indicator above every “day” data shows the general sentimental state, while every small square represents one post: dark green to light green indicates a somewhat positive to very positive post, dark pink to bright pink a bit negative, to an extremely negative post. Shades of grey indicates neutral posts. Posts are ordered by time.

This provides an interesting overview of emotion per day, and also over time. Individual posts are nice but can get quite overwhelming with time (as your Slack community’s activity grows).

Version 2

Version 2: hours of the week are on the X-axis. Every square onthe Y-axis represents the number of posts of a specific polarity level.

Similar to the previous visualisation, the above image represents activity per weekday, but now the X-axis is used to represent the hours of the week (e.g. there is a total of 7 x 24 columns of squares). Color brightness indicates the number of posts for a specific sentimental polarity level: grey to white indicates the number of neutral posts, dark green to bright green indicates the number of positive posts (brighter colors equal more posts). The height represents the level of sentiment e.g. a green dot near the top is a very positive post, while a green dot near the center (near the neutral posts) indicate a somewhat positive post.

While information on individual posts is lost, it is easier to see the distribution of levels of emotions per hour of a specific weekday. Great, but can we do better?

Version 3: The Emotional Fingerprint

(currently) The final version. Every user has a 7 column fingerprint. The larger the Y-range, the more spread your posts are emotionally-wise!

Condensing the data even more, the Emotional Fingerprint visualisation gives every Slack user a unique overview of their emotional state across the entire dataset per weekday (7 columns for 7 weekdays). While giving personal insights, the Emotional Fingerprint presents an easy way of comparing individuals and could help find patterns in larger communities, or even across communities. As mentioned before, we’d love for you, those Slack communities, and any other really, to get in touch!

This is a slightly extended, less “dry” version of my post on , our research group’s website.

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.