Data from: In the mood: the dynamics of collective sentiments on Twitter

We study the relationship between the sentiment levels of Twitter users
and the evolving network structure that the users created by @-mentioning
each other. We use a large dataset of tweets to which we apply three
sentiment scoring algorithms, including the open source SentiStrength
program. Specifically we make three contributions. Firstly, we find that
people who have potentially the largest communication reach (according to
a dynamic centrality measure) use sentiment differently than the average
user: for example, they use positive sentiment more often and negative
sentiment less often. Secondly, we find that when we follow structurally
stable Twitter communities over a period of months, their sentiment levels
are also stable, and sudden changes in community sentiment from one day to
the next can in most cases be traced to external events affecting the
community. Thirdly, based on our findings, we create and calibrate a
simple agent-based model that is capable of reproducing measures of
emotive response comparable with those obtained from our empirical
dataset.