Topic Modeling
Topic modeling is an unsupervised machine learning technique used to discover abstract topics within a collection of documents. It automatically identifies patterns of words that frequently occur together, grouping them into topics that represent the main themes in the text data. Common algorithms include Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF).
Developers should learn topic modeling when working with large text datasets for tasks like document clustering, content recommendation, or trend analysis in fields such as social media monitoring, customer feedback analysis, or academic research. It's particularly useful for extracting insights from unstructured text without predefined labels, enabling automated summarization and organization of textual information.