Topic modeling algorithms such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) examine the co-occurrence of words in a set of documents (in this case, emails) to identify clusters of words that frequently appear together. These clusters represent underlying topics. Hereâs a simplified workflow:
Collect a large set of emails. Preprocess the text by removing stop words, stemming, and tokenizing. Apply a topic modeling algorithm to the preprocessed text. Review the output to interpret the topics and their relevance.