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Out of the estimated 926 million Americans that use airports as their transportation systems, 27 million of them have a disability. People with disabilities face many barriers in the built environment, especially within public transportation (eg, airports, train stations, bus terminals). This study applies unique text mining techniques to discover the topics that travelers with disabilities experienced. The authors collected and analyzed 553,430 reviews for 67 international airports across the United States to find the possible inequity issues involving passengers with disabilities. As online reviews are typically shorter in nature than other forms of social media, a unique short text clustering method (a collapsed Gibbs sampling algorithm for the Dirichlet multinomial mixture model, GSDMM) was used to identify 10 topics through 3,516 unique nouns. The results showed that topics involving restrooms and intra-airport navigation score lower for travelers with disabilities than with all travelers. The approach and findings can be used practically to identify concerns for designers and airport operational entities to find potential concerns to mitigate the inequity issues of passengers with disability.