Human emotional states are not independent but rather proceed along systematic paths governed by both internal, cognitive factors and external, social ones. For example, anxiety often transitions to disappointment, which is likely to sink to depression before rising to happiness and relaxation, and these states are conditioned by the states of others in our communities. Modeling these complex dependencies can yield insights into human emotion and support more powerful sentiment technologies.

We develop a theory of conditional dependencies between emotional states in which emotions are characterized not only by valence (polarity) and arousal (intensity) but also by the role they play in state transitions and social relationships. We implement this theory using conditional random fields (CRFs) that synthesize textual information with information about previous emotional states and the emotional states of others. To assess the power of affective transitions, we evaluate our model in a collection of 'mood' updates from the Experience Project. To assess the power of social factors, we use a corpus of product reviews from a website in which the community dynamics encourage reviewers to be influenced by each other. In both settings, our models yield improvements of statistical and practical significance over ones that classify each text independently of its emotional or social context.