LDA is a foundational algorithm used in topic modeling, defined as a Bayesian approach to this task. It uses probabilistic inference to uncover latent topics within a corpus, or collection of documents. LDA operates as a conditional probabilistic model, classifying words and documents into topics based on underlying probability distributions.
LDA is characterized as a soft or fuzzy clustering algorithm. This means that documents and individual words are not assigned exclusively to one topic but can belong to multiple topics, each with varying probabilities. As an unsupervised machine learning technique, LDA does not require pre-labeled data. Documents are input, and output validation relies on interpretability and external evaluation. The process requires moderate preprocessing, including steps like tokenization and removal of stop words.
LDA is typically considered a classic, widely studied “bag-of-words” approach. It generally performs best for longer documents or large corpora, such as academic papers or reports. When contrasted with models like BERTopic, a benefit of LDA is that it allows for documents to have mixed membership across topics, making it suitable for representing complex or multifaceted documents. The parameters (hyperparameters) defining the number of topics must be explicitly specified when using LDA.