Techniques & Methods
Topic Modeling
Topic modeling algorithms (like LDA, NMF, and BERTopic) analyze large corpora to uncover latent thematic structure—grouping documents by underlying subjects without human labeling. Each topic is represented as a probability distribution over words.
Applications include content categorization, trend analysis, customer feedback analysis, and search result clustering. Modern neural approaches using sentence embeddings have significantly improved topic coherence over classical methods.
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Related Terms
Core Concepts
Unsupervised Learning
Models learn patterns from unlabeled data without explicit instructions.
Core Concepts
Latent Variables
Hidden or unobservable variables inferred from observable data in AI models.
Techniques & Methods
Text Classification
Automatically assigning predefined categories to text documents.
Techniques & Methods
Information Extraction
Automatically extracting structured information from unstructured text.