English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Emotions are experienced and expressed through various response systems.Coherence between emotional experience and emotional expression is consideredimportant to clients' well being. To date, emotional coherence (EC) has beenstudied at a single time point using lab-based tasks with relatively smalldatasets. No study has examined EC between the subjective experience ofemotions and emotion expression in therapy or whether this coherence isassociated with clients' well being. Natural language Processing (NLP)approaches have been applied to identify emotions from psychotherapy dialogue,which can be implemented to study emotional processes on a larger scale.However, these methods have yet to be used to study coherence between emotionalexperience and emotional expression over the course of therapy and whether itrelates to clients' well-being. This work presents an end-to-end approach wherewe use emotion predictions from our transformer based emotion recognition modelto study emotional coherence and its diagnostic potential in psychotherapyresearch. We first employ our transformer based approach on a Hebrewpsychotherapy dataset to automatically label clients' emotions at utterancelevel in psychotherapy dialogues. We subsequently investigate the emotionalcoherence between clients' self-reported emotional states and our model-basedemotion predictions. We also examine the association between emotionalcoherence and clients' well being. Our findings indicate a significantcorrelation between clients' self-reported emotions and positive and negativeemotions expressed verbally during psychotherapy sessions. Coherence inpositive emotions was also highly correlated with clients well-being. Theseresults illustrate how NLP can be applied to identify important emotionalprocesses in psychotherapy to improve diagnosis and treatment for clientssuffering from mental-health problems.

NLP meets psychotherapy: Using predicted client emotions and self-reported client emotions to measure emotional coherence