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Unsupervised Text Classification for Marketing Analytics

University of Colorado Boulder via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

13 hours 28 minutes
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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Discover patterns in marketing text without predefined outcome labels by combining topic modeling with text-derived network analysis. Learners build and inspect TF-IDF representations, select topic counts and membership assumptions, evaluate and interpret topic solutions, and construct directed or weighted networks from word co-occurrences and user mentions to identify central and bridging structures.

Syllabus

What is topic modeling?

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.

The Assumptions of a Topic Model, Bag of Words, and Natural Language Processing

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.

Prepping Amazon Review Data

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.

Pre-Processing Text and Training a Topic Model

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.

Topic Modeling Evaluation, Classification, and Neural Network Approaches

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.