TopicModellen
Topic modeling is a type of statistical model that is used to uncover abstract "topics" that occur in a collection of documents. It is a form of unsupervised learning, meaning it doesn't require pre-labeled data. The core idea is to represent each document as a mixture of topics and each topic as a mixture of words. When a document is analyzed, the topic model identifies the most probable topics present in it based on the words it contains. Similarly, a topic is characterized by a set of words that are likely to appear together frequently.
Common algorithms for topic modeling include Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (pLSA), and
Topic modeling finds applications in various fields. It can be used for organizing large archives of text,