(this work was realised in 2015)
Sentiment analysis provides interesting insights into social network behaviour. The team I worked with at university used Slack as its main means of communication, the perfect test bed for exploring new and interesting ways of visualising the emotional state and distribution based on our active discussions on Slack (do we get to see proof of the mental state of academics? ;))
I'll run through the versions that led to the final design.
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. Another interesting approach is to discard individual posts, and look for more general tendencies in the data.
Similar to the previous visualisation, the above image represents activity per weekday. Color brightness now 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. Each column represents an hour of a weekday (e.g. there is a total of 7 x 24 columns).
While information on individual posts is lost, it is easier to see the distribution of levels of emotions per hour, per weekday. This lead to the following visualisation.
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.
It's obvious that during the PhD, I was not a happy camper on Mondays ;) (middle column)