Topic modelling

Topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), identify underlying themes or subjects within a set of documents without requiring predefined labels. LDA, for instance, treats each document as a mixture of topics and each topic as a mixture of words. The algorithm infers the topic distribution for each document and the word distribution for each topic. The process involves iteratively refining the topic assignments to words and document assignments to topics until a stable state is reached. These models are valuable for tasks like document classification, information retrieval, and understanding large collections of text data. They provide a way to automatically organize and summarize text corpora, revealing hidden thematic structures that might not be immediately apparent through manual inspection. The quality of topic models is often evaluated using metrics like perplexity and topic coherence.