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.
@article{arxiv.2204.10189,
title = {Neural Topic Modeling of Psychotherapy Sessions},
author = {Baihan Lin and Djallel Bouneffouf and Guillermo Cecchi and Ravi Tejwani},
journal= {arXiv preprint arXiv:2204.10189},
year = {2022}
}
Comments
This work extends our research series in computational linguistics for psychiatry (e.g. working alliance analysis in arXiv:2204.05522) with a systematic investigation of neural topic modeling approaches to provide interpretable insights in psychotherapy