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, ...

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