This paper describes analyses of a corpus of speech recorded during psychotherapy. The therapy sessions were focused on addressing unresolved anger towards an attachment figure. Speech from the therapy sessions of 22 young adult females was initially recorded, from which 283 stimuli were extracted and submitted for evaluation of emotional content by 14 judges. The emotional content was rated on three scales: Activation, Valence and Dominance. A set of acoustic features was then extracted: statistic features, F0 features based on the Fujisaki model and perceptual speech rate features. The relationship between acoustics and emotional content was examined through correlation analysis and automatic classification. Results of the model-based analysis shows significant correlations between the strength and frequency of accents and Activation, as well between base F0 and dominance. Automatic classification showed that the acoustic features were better at predicting Activation rather than Valence and Dominance, and that the dominant features were those based on F0.