Elucidating Emotional Patterns in Autism Spectrum Disorder: BERT-Based Analysis Reveals Novel Dimensional Structure

BackgroundAutism spectrum disorder (ASD) is associated with difficulties in emotion recognition and regulation, which complicates clinical support and treatment. While natural language processing (NLP) has enabled automated emotion analysis, few studies have investigated emotion structure in ASD using dimensional approaches. ObjectiveTo develop a BERT-based model for estimating eight-dimensional emotion profiles and to apply this model to clinical records of adolescents with ASD to elucidate characteristic affective patterns. MethodsWe fine-tuned five Japanese-language BERT variants using the WRIME dataset, which contains annotations for eight basic emotions with graded intensity. The best-performing model was applied to clinical records from 14 adolescents with ASD, yielding emotion profile vectors for 1,239 clinical sessions. Principal component analysis (PCA) was conducted on the resulting emotion vectors to identify dominant affective dimensions. ResultsThe best-performing model (tohoku-nlp/bert-large-japanese-v2) achieved an accuracy of 78.9% and a cosine similarity of 94.1%. PCA revealed a primary emotional dimension dominated by sadness, and a secondary axis characterized by a contrast between disgust and anticipation. These patterns diverge from canonical emotion models such as Plutchiks circumplex and suggest a distinct emotional architecture in ASD. ConclusionThis study demonstrates the utility of fine-tuned BERT models in extracting nuanced emotion profiles from clinical text. The identified emotional dimensions may provide a basis for developing more personalized support strategies for individuals with ASD.