BERT meets LIWC: Exploring State-of-the-Art Language Models for Predicting Communication Behavior in Couples’ Conflict Interactions
Type
conference paper
Date Issued
2021-10-18
Author(s)
Biggiogera, Jacopo
;
Boateng, George
;
Hilpert, Peter
;
Vowels, Matthew
;
Bodenmann, Guy
;
Neysari, Mona
;
Nussbeck, Fridtjof
;
Abstract
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, and produces sparse data which has forced the field to use average behaviors across entire interactions, thereby undermining the ability to study processes on a fine-grained scale. 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.
Language
English
Keywords
Couples
predicting observer ratings
multimodal fusion
behavioral signal processing
BERT
LIWC
SVM
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Book title
Companion Publication of the 2021 International Conference on Multimodal Interaction
Publisher
ACM
Publisher place
New York, NY, USA
Start page
385
End page
389
Event Title
ACM 2021 International Conference on Multimodal Interaction
Event Location
Montréal, QC, Canada
Event Date
October 18–22, 2021
Official URL
Division(s)
Eprints ID
265543
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Name
Biggiogera et al 2021 BERT LIWC Couple Conflict.pdf
Size
433.25 KB
Format
Adobe PDF
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