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

TL;DR

This paper demonstrates how natural language processing techniques, including emotion detection and topic modeling, can assist psychologists in analyzing and understanding the challenges faced by young people with intellectual or developmental disabilities during their transition to adulthood.

Contribution

The study introduces the application of unsupervised NLP methods to analyze emotions and topics in conversational data related to young people with IDD, providing new tools for psychological assessment.

Findings

NLP methods effectively analyze emotions in conversations.

Topic modeling reveals common issues faced by young people with IDD.

Comparison shows differences between individuals with and without IDD.

Abstract

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.