Topic modeling is an unsupervised technique and might be useful if you need to analyze a large archive of text documents and wish to understand what the archive contains, without necessarily reading every single document by yourself. A text document can be a blog post, an email, a tweet, a document, a book chapter, a diary entry, and so on. Topic modeling looks for patterns in a corpus of text; more precisely, it identifies topics as lists of words that appear in a statistically meaningful way. The most well-known algorithm is Latent Dirichlet Allocation (LDA), which assumes that the author composed a piece of text by selecting words from possible baskets of words, where each basket corresponds to a topic. Using this assumption, it becomes possible to mathematically decompose text into the most likely baskets from where the words first came. The algorithm then iterates over this process until it converges to the most likely distribution of words into baskets, which we call topics.

For example, if we use topic modeling on a series of news articles, the algorithm would return a list of topics and keywords that most likely comprise of these topics. Using the example of news articles, the list might look similar to the following:

• Winner, goal, football, score, first place
• Company, stocks, bank, credit, business
• Election, opponent, president, debate, upcoming

By looking at the keywords, we can recognize that the news articles were concerned with sports, business, upcoming election, and so on. Later in this chapter, we will learn how to implement topic modeling using the news article example.