Topic Modeling
An unsupervised technique that automatically discovers abstract themes (topics) in a collection of documents. Each document is represented as a mixture of topics.
Why It Matters
Topic modeling reveals what your document collection is about without reading every document. It enables content organization, trend detection, and information discovery at scale.
Example
LDA applied to 100,000 news articles discovering topics like 'elections' (vote, candidate, poll), 'climate' (carbon, temperature, energy), and 'tech' (AI, startup, data).
Think of it like...
Like sorting a huge pile of mail into categories based on content — the system figures out the natural groupings without being told what to look for.
Related Terms
Unsupervised Learning
A type of machine learning where the model learns patterns from unlabeled data without being told what the correct output should be. The algorithm discovers hidden structures, groupings, or patterns in the data on its own.
Clustering
An unsupervised learning technique that groups similar data points together based on their characteristics, without predefined labels. The algorithm discovers natural groupings in the data.
Text Mining
The process of deriving meaningful patterns, trends, and insights from large collections of text data using NLP and statistical techniques.
Natural Language Processing
The branch of AI that deals with the interaction between computers and human language. NLP enables machines to read, understand, generate, and make sense of human language in a useful way.