Neural Topic Modeling of Psychotherapy Sessions
Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Ravi Tejwani
In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings. We also incorporate temporal modeling to put this additional interpretability to action by parsing out topic similarities as a time series in a turn-level resolution. We believe this topic modeling framework can offer interpretable insights for the therapist to optimally decide his or her strategy and improve psychotherapy effectiveness.
cs.CL, cs.AI, cs.HC, cs.LG, q-bio.NC