Latent Dirichlet Allocation for Identifying Reentry Employer Fit

Latent Dirichlet Allocation, commonly called LDA, is a topic modeling technique that identifies hidden themes across large collections of text by analyzing which words tend to appear together. When applied to job postings and employer reviews, LDA can surface which companies consistently discuss values like second chances, growth mindset, or fair chance hiring even when those exact phrases are not used.

For people with records, this means AI tools can scan hundreds of job postings and identify employers whose language patterns suggest genuine openness to reentry candidates rather than surface-level compliance with fair chance policies. It gives job seekers a smarter starting point for targeting applications where their background is least likely to be a barrier.