Tweeting emotions: sentiment in Twitter events

Thelwall*, Buckley and Paltoglou (it's a preprint of a paper which is going to be published in JASIST soon) studied the emotional responses in 34, 770, 790 Twitter messages, tweeted from February 9 to March 9, 2010. The main two events in the media that month were the Oscars and the Winter Olympics.

They identified the 30 most popular events (biggest spikes of interest) and tried to classify the sentiment strength expressed in the tweets using an algorithm called SentiStrength. SentiStrength was developed for analysis of MySpace comments, which means it is most suitable for analysing, shall we say, English that didn't come straight out of the Oxford dictionary. SentiStrength measures emotions with two 1-5 scales, one for positive sentiment and one for negative.

As far as emotions on Twitter go, important events mean mainly an increase in negativity, with heavier traffic hours having stronger negativity sentiment. Also, hours after the traffic's peak have stronger sentiment than the hours before. There was also a correlation between higher traffic hours and stronger positive sentiment, in comparison with the hours before, but it wasn't as strong as the correlations found for negative sentiment.

The authors conclude that "important events in Twitter are associated with increases in average negative sentiment strength".

Increase in negative sentiment strength doesn't always say a decrease in positive sentiment, because people can see an event from various view points (is Sandra Bullock winning the Oscar good or bad? Depends on the person asked).

Unfortunately, the authors weren't able to identify the importance of an event from the strength of the expressed emotions. While statistically significant, the changes in emotion strength related to popular events were only around 1% and "were far from universal". The authors' conclusion was that tweeting about events is less about emotional "gut" reactions than about "affording posters opportunities to satisfy personal goals" (say, making a joke).

Thelwall, M., Buckley, K., & Paltoglou, G (2010). Sentiment in Twitter events JASIST

*same disclosure as last post.