Transition to Adulthood for Young People with Intellectual or Developmental Disabilities: Emotion Detection and Topic Modeling
It addresses the challenges faced by young people with IDD and their families during a critical life stage, but is incremental as it applies existing NLP methods to a new domain.
This study applied unsupervised NLP methods, including emotion detection and topic modeling, to analyze conversational data from young people with intellectual or developmental disabilities (IDD) and their families during the transition to adulthood, finding these tools useful for psychologists in analyzing emotions and identifying key topics, with results compared to non-IDD peers.
Transition to Adulthood is an essential life stage for many families. The prior research has shown that young people with intellectual or development disabil-ities (IDD) have more challenges than their peers. This study is to explore how to use natural language processing (NLP) methods, especially unsupervised machine learning, to assist psychologists to analyze emotions and sentiments and to use topic modeling to identify common issues and challenges that young people with IDD and their families have. Additionally, the results were compared to those obtained from young people without IDD who were in tran-sition to adulthood. The findings showed that NLP methods can be very useful for psychologists to analyze emotions, conduct cross-case analysis, and sum-marize key topics from conversational data. Our Python code is available at https://github.com/mlaricheva/emotion_topic_modeling.