2020/06/04 by Danai Xezonaki, D. Xezonaki, Georgios Paraskevopoulos +9
Computer Science · Mathematics · Psychology · #Computation and Language (cs.CL) #Emotion and Mood Recognition #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mental Health via Writing #Sentiment Analysis and Opinion Mining #cs.CL #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2006.08336
arxiv created 2020/06/04 · openalex publication_date 2020/06/04 · arxiv updated 2020/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work we propose a machine learning model for depression detection from transcribed clinical interviews. Depression is a mental disorder that impacts not only the subject's mood but also the use of language. To this end we use a Hierarchical Attention Network to classify interviews of depressed subjects. We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica. Our analysis shows that individuals diagnosed with depression use affective language to a greater extent than not-depressed. Our experiments show that external affective information improves the performance of the proposed architecture in the General Psychotherapy Corpus and the DAIC-WoZ 2017 depression datasets, achieving state-of-the-art 71.6 and 68.6 F1 scores respectively.