Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy

OBJECTIVE

Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts.

METHOD

We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses.

RESULTS

The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination.

DISCUSSION

Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.

A multi-modality and multi-dyad approach to measuring flexibility in psychotherapy

INTRODUCTION

Flexibility, the ability of an individual to adapt to environmental changes in ways that facilitate goal attainment, has been proposed as a potential mechanism underlying psychopathology and psychotherapy. In psychotherapy, most findings are based on self-report measures that have important limitations. We propose a multimodal, multi-dyad approach based on a nonlinear dynamical systems framework to capture the complexity of this concept.

METHOD

A new research paradigm was designed to explore the validity of the proposed conceptual model. The paradigm includes a psychotherapy-like social interaction, during which body movement and facial expressiveness data were collected. We analyzed the data using Hankel Alternative View of Koopmann analysis to reconstruct attractors of the observed behaviors and compare them.

RESULTS

The patterns of behavior in the two cases differ, and differences in the reconstructed attractors correspond with differences in self-report measures and behavior in the interactions.

CONCLUSIONS

The case studies show that information provided by a single modality is not enough to provide the full picture, and multiple modalities are needed. These observations can serve as an initial support for our claims that a multi-modal and multi-dyad approach to flexibility can address some of the issues of measurement in the field.

Multiple-Time-Scale Analysis of Attention as Revealed by EEG, NIRS, and Pupil Diameter Signals During a Free Recall Task: A Multimodal Measurement Approach

Attention plays a fundamental role in acquiring and understanding information. Therefore, it is useful to evaluate attention objectively in such fields as education and mental health. Aimed at extracting objective indicators of attention from physiological signals, this study examined the characteristics of electroencephalography (EEG), near-infrared spectroscopy (NIRS), and pupil diameter signals during a free recall task. The objective was to clarify the temporal characteristics of these signals in relation to attention. We used a free recall task as a cognitive task with an attentional load. The participants attempted to memorize and then recall 13 serially presented words. Our hypothesis was that the significant physiological responses should differ depending on the time scale of the attention evaluation. The physiological responses were compared on the basis of differences between success and failure to recall a word on a short time scale, in terms of the attentional state among five serial position groups on a middle time scale, and on the basis of differences between trials with many and few words recalled on a long time scale. We found that the response of each physiological signal depended on the attention in the different time-scale comparisons. (1) The P300 amplitudes of the EEG signals for the words that were recalled were significantly higher than those for the words that were not recalled. (2) Pupillary dilation differed significantly depending on the serial position group. (3) Functional connectivity in the right hemisphere revealed by NIRS was significantly stronger in trials with many words recalled than in those with few words recalled. Different temporal characteristics of physiological signals with respect to attention were suggested by multimodal measurement and multiple-time-scale analysis. Consideration of these characteristics should help in the development of applications requiring objective attention evaluation.