Topic models provide a simple way to analyze large volumes of unlabeled text. A “topic” consists of a cluster of words that frequently occur together. Using contextual clues, topic models can connect words with similar meanings and distinguish between uses of words with multiple meanings.
The MALLET topic model package includes an extremely fast and highly scalable implementation of Gibbs sampling, efficient methods for document-topic hyperparameter optimization, and tools for inferring topics for new documents given trained models.