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Unsupervised Text Classification for Marketing Analytics

Unsupervised Text Classification for Marketing Analytics

This course is part of Text Marketing Analytics Specialization

Instructors: Chris J. Vargo

Included with

Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

Describe the concept of topic modeling and related terminology (e.g., unsupervised machine learning)
Apply topic modeling to marketing data via a peer-graded project
Apply topic modeling to a variety of popular marketing use cases via homework assignments
Evaluate, tune and improve the performance the topic model you create for your project

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

2 assignments

Taught in English

91%

of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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Build your subject-matter expertise

This course is part of the Text Marketing Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project.

This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment. Files are stored in Google Drive. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Module details

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.

What's included

2 videos5 readings1 programming assignment1 discussion prompt

2 videosTotal 21 minutes

Welcome to the Course!1 minute
Topic Modeling Lecture 120 minutes

5 readingsTotal 41 minutes

Course Updates and Accessibility Support1 minute
Earn Academic Credit for your Work!10 minutes
Course Support10 minutes
Introduction to Using Google Colab for this Course10 minutes
Dr. Vargo's Topic Modeling Approach to YikYak Data10 minutes

1 programming assignmentTotal 180 minutes

Homework 1: Segmenting by Sentiment180 minutes

1 discussion promptTotal 10 minutes

Introduce Yourself!10 minutes

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.

What's included

2 videos1 reading1 assignment1 programming assignment

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.

What's included

2 videos2 readings1 assignment

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.

What's included

2 videos2 readings1 peer review

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.

What's included

3 videos3 readings1 peer review

3 videosTotal 37 minutes

Topic Modeling Lecture 815 minutes
Topic Modeling Lecture 99 minutes
Topic Modeling Lecture 1013 minutes

3 readingsTotal 80 minutes

Lecture Notebook Links10 minutes
Papers (1, 2, and 3) on Topic Modeling Fit Statistics10 minutes
Lab 3: Topic Modeling with BERTopic60 minutes

1 peer reviewTotal 10 minutes

Lab 3 Peer Review: BERTopic Visualizations10 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹