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Transition to Adulthood is an essential life stage for many families. Theprior research has shown that young people with intellectual or developmentdisabil-ities (IDD) have more challenges than their peers. This study is toexplore how to use natural language processing (NLP) methods, especiallyunsupervised machine learning, to assist psychologists to analyze emotions andsentiments and to use topic modeling to identify common issues and challengesthat young people with IDD and their families have. Additionally, the resultswere compared to those obtained from young people without IDD who were intran-sition to adulthood. The findings showed that NLP methods can be veryuseful for psychologists to analyze emotions, conduct cross-case analysis, andsum-marize key topics from conversational data. Our Python code is available athttps://github.com/mlaricheva/emotion_topic_modeling.

Transition to Adulthood for Young People with Intellectual or Developmental Disabilities: Emotion Detection and Topic Modeling