Python Data Analysis Cookbook

Extracting topics with non-negative matrix factorization

Topics in natural language processing don't exactly match the dictionary definition and correspond to more of a nebulous statistical concept. We speak of topic models and probability distributions of words linked to topics, as we know them. When we read a text, we expect certain words that appear in the title or the body of the text to capture the semantic context of the document. An article about Python programming will have words like "class" and "function", while a story about snakes will have words like "eggs" and "afraid." Texts usually have multiple topics; for instance, this recipe is about topic models and non-negative matrix factorization, which we will discuss shortly. We can, ...