  Adult learning is increasingly recognized as a crucial way for personal
development and societal progress. It however is challenging, and adult
learners face unique challenges such as balancing education with other life
responsibilities. Collecting feedback from adult learners is effective in
understanding their concerns and improving learning experiences, and social
networks provide a rich source of real-time sentiment data from adult learners.
Machine learning technologies especially large language models (LLMs) perform
well in automating sentiment analysis. However, none of such models is
specialized for adult learning with accurate sentiment understanding. In this
paper, we present A-Learn, which enhances adult learning sentiment analysis by
customizing existing general-purpose LLMs with domain-specific datasets for
adult learning. We collect adult learners' comments from social networks and
label the sentiment of each comment with an existing LLM to form labelled
datasets tailored for adult learning. The datasets are used to customize
A-Learn from several base LLMs. We conducted experimental studies and the
results reveal A-Learn's competitive sentiment analysis performance, achieving
up to 91.3% accuracy with 20% improvement over the base LLM. A-Learn is also
employed for word cloud analysis to identify key concerns of adult learners.
The research outcome of this study highlights the importance of applying
machine learning with educational expertise for teaching improvement and
educational innovations that benefit adult learning and adult learners.
