Abstract
In this paper, a new modeling and learning approach is presented which is based on two assumptions from the field of psychology: 1. The number of Tweets mainly depends on previous dynamics of the discussion, i.e. a state-space modeling approach is used for the first time. 2. Humans mainly react to emotional stimuli, i.e. Tweets are automatically characterized by their emotional content. Therefore, the emotions of conversations are extracted and used for system identification and parameter estimation of a state space model, which deals with events and its transitions.
