Analysis of topic propagation in therapy sessions using Partially Labeled Dirichlet Allocation

The full comprehension of how topics change within psychotherapeutic conversation is key for assessment and therapeutic strategies to adopt by the counselor to the patients. That might enable artificial intelligence (AI) approaches to recommend the most suitable strategy for a new patient. Basically, understanding the topics dynamics of previous cases allows choosing the best therapy to perform for new patients depending on their current conversations. In this paper we leverage Partially Labeled Dirichlet Allocation with the goal to detect and track topics in real-life psychotherapeutic conversations. On the one hand, the detection of topics allows us identifying the semantic themes of the current therapeutic conversation and predicting topics ad-hoc for each talk-turn between the patient and the counselor. On the other hand, the tracking of topics is key to understand and explore the dynamics of the conversation giving insights and tips on logic and strategy to adopt. We point out that the entire conversation is structured and modeled according to a sequence of ongoing topics that might propagate through each talk-turn. We present a new method that combines topic modeling and transitions matrices that gives important information to counselors for their therapeutic strategies.