DTSA 5799 Unsupervised Text Classification for Marketing Analytics

In this module, we will cover the fundamental concepts of topic modeling, also known as unsupervised machine learning on unstructured text documents. We will contrast unsupervised methods to supervised ones and survey common applications of topic modeling.

In this module,  we will go under the hood inside a topic modeling approach and understand what assumptions drive topic model fit. We will also uncover how bag-of-words approaches to topic modeling work, and the natural language processing required to produce meaningful topic modeling features.

In this module, we will cover how to parse through JSON-like data and segment it to create a corpus that is ready for the topic modeling process. We will cover how the data for your project is structured and its taxonomy.

In this module, we will take Amazon review data and load it into a corpus to preprocess it. We will cover how to build topic models from the data and also save those topic models.

In this module, we will learn how to evaluate the fit of topic models and use the best topic model to classify documents. We will also cover how to build topic models with pre-trained neural networks.

You will complete a peer reviewed project worth 20% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.