Machine Learning in Java - Second Edition

Topic modeling

Topic modeling is an unsupervised technique and might be useful if you need to analyze a large archive of text documents and wish to understand what the archive contains, without necessarily reading every single document by yourself. A text document can be a blog post, an email, a tweet, a document, a book chapter, a diary entry, and so on. Topic modeling looks for patterns in a corpus of text; more precisely, it identifies topics as lists of words that appear in a statistically meaningful way. The most well-known algorithm is Latent Dirichlet Allocation (LDA), which assumes that the author composed a piece of text by selecting words from possible baskets of words, where each basket corresponds to a topic. Using this assumption, ...