  Many processes in psychology are complex, such as dyadic interactions between
two interacting partners (e.g. patient-therapist, intimate relationship
partners). Nevertheless, many basic questions about interactions are difficult
to investigate because dyadic processes can be within a person and between
partners, they are based on multimodal aspects of behavior and unfold rapidly.
Current analyses are mainly based on the behavioral coding method, whereby
human coders annotate behavior based on a coding schema. But coding is
labor-intensive, expensive, slow, focuses on few modalities. Current approaches
in psychology use LIWC for analyzing couples' interactions. However, advances
in natural language processing such as BERT could enable the development of
systems to potentially automate behavioral coding, which in turn could
substantially improve psychological research. In this work, we train machine
learning models to automatically predict positive and negative communication
behavioral codes of 368 German-speaking Swiss couples during an 8-minute
conflict interaction on a fine-grained scale (10-seconds sequences) using
linguistic features and paralinguistic features derived with openSMILE. Our
results show that both simpler TF-IDF features as well as more complex BERT
features performed better than LIWC, and that adding paralinguistic features
did not improve the performance. These results suggest it might be time to
consider modern alternatives to LIWC, the de facto linguistic features in
psychology, for prediction tasks in couples research. This work is a further
step towards the automated coding of couples' behavior which could enhance
couple research and therapy, and be utilized for other dyadic interactions as
well.
