  Tenant-landlord relationships exhibit a power asymmetry where landlords'
power to evict the tenants at a low-cost results in their dominating status in
such relationships. Tenant concerns are thus often unspoken, unresolved, or
ignored and this could lead to blatant conflicts as suppressed tenant concerns
accumulate. Modern machine learning methods and Large Language Models (LLM)
have demonstrated immense abilities to perform language tasks. In this study,
we incorporate Latent Dirichlet Allocation (LDA) with GPT-4 to classify Reddit
post data scraped from the subreddit r/Tenant, aiming to unveil trends in
tenant concerns while exploring the adoption of LLMs and machine learning
methods in social science research. We find that tenant concerns in topics like
fee dispute and utility issues are consistently dominant in all four states
analyzed while each state has other common tenant concerns special to itself.
Moreover, we discover temporal trends in tenant concerns that provide important
implications regarding the impact of the pandemic and the Eviction Moratorium.
