  Evolutionary clustering aims at capturing the temporal evolution of clusters.
This issue is particularly important in the context of social media data that
are naturally temporally driven. In this paper, we propose a new probabilistic
model-based evolutionary clustering technique. The Temporal Multinomial Mixture
(TMM) is an extension of classical mixture model that optimizes feature
co-occurrences in the trade-off with temporal smoothness. Our model is
evaluated for two recent case studies on opinion aggregation over time. We
compare four different probabilistic clustering models and we show the
superiority of our proposal in the task of instance-oriented clustering.
