Visualizing Moods on Twitter

Sune Lehmann, of the Barabasi Lab, pointed me to some recent work on detecting and visualizing ‘moods’ in Twitter: Twitter Mood:

We estimate the mood of all tweets for which the author has provided a location. The mood (valence) of each individual tweet is based on the Affective Norms for English Words (ANEW) data set. ANEW is a set of 1034 words, previously identified as bearing emotional weight (e.g. abuse, acceptance, accident). The mood of each word was estimated in a study at the University of Florida, where participants (college students), were shown lists of isolated words and asked to grade each word’s valence, arousal, and dominance level on an integer scale of 1-9. The mood of a tweet is calculated as the average valence ANEW words included in the tweet (if any).

I don’t see any information on the site regarding the accuracy of the mood detection algorithm. My guess would be that there are plenty of words out there that are not in the ANEW lexicon – it’d be interesting to see that study. The visualizations are both temporal and geographic:

This is, of course, similar to the work on tracking ‘moods’ in LiveJournal done at MoodViews.

For visualizations like this, I’d be interested in seeing some discussion as to how to ‘read’ the presentation, and for evidence of its predictive power.

At any rate, I’m looking forward to seeing how this develops.

Comments

[ian]

So, what would you _do_ with a mood prediction if you had one? Spike the water? Not start a health care reform debate?