Topic modeling with Latent Dirichlet Allocation
Topic modeling describes the broad task of assigning topics to unlabelled text documents. For example, a typical application would be the categorization of documents in a large text corpus of newspaper articles where we don't know on which specific page or category they appear in. In applications of topic modeling, we then aim to assign category labels to those articles—for example, sports, finance, world news, politics, local news, and so forth. Thus, in the context of the broad categories of machine learning that we discussed in Chapter 1, Giving Computers the Ability to Learn from Data, we can consider topic modeling as a clustering task, a subcategory of unsupervised learning.
In this section, we will introduce a popular technique for topic modeling called Latent Dirichlet Allocation (LDA). However, note that while Latent Dirichlet Allocation is often abbreviated as LDA, it is not to be confused with Linear discriminant analysis, a supervised...
