Topic Modeling

Topic modeling discovers abstract topics that occur in a collection of documents (corpus) using a probabilistic model. It’s frequently used as a text mining tool to reveal semantic structures within a body of text.

A document about a specific topic will have certain words appearing more frequently than others. For example, an article about topic modeling will frequently mention words such as “model,” “algorithm,” “text,” “data,” and “analysis.” But it is very unlikely to use words such as “pride and prejudice” (an 1813 romantic novel by Jane Austen) unless it’s incorporated as an example.

Terms exhibiting similarity are grouped together and the topic determined based on the statistical probability of occurrence of those words.

Documents aren’t restricted to discussing only one issue. Frequently they address multiple topics. In the case of an article about “topic modeling and SEO,” it will likely employ terms like “search engines,”  “optimization,” and “SEO” in addition to those previously mentioned topic model words.

Topic models reveal latent semantic structures and offer insights into unstructured data, the type of data that pervades the internet. Some popular topic models include LDA (latent Dirichlet allocation ), LSA (latent semantic analysis), and TF-IDF (term frequency-inverse document frequency).

Topic Modeling with Latent Dirichlet Allocation in Python