This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.
Text Mining and Analytics
Text Mining and Analytics
This course is part of Data Mining Specialization
Instructor: ChengXiang Zhai
72,031 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.5
(727 reviews)
33 hours to complete
3 weeks at 11 hours a week
Flexible schedule
Learn at your own pace
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Assessments
14 assignments
Taught in English
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This course is part of the Data Mining Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 7 modules in this course
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
What's included
2 videos5 readings2 assignments1 plugin
During this module, you will learn the overall course design, an overview of natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications, and word association mining with a particular focus on mining one of the two basic forms of word associations (i.e., paradigmatic relations).
What's included
9 videos1 reading2 assignments
During this module, you will learn more about word association mining with a particular focus on mining the other basic form of word association (i.e., syntagmatic relations), and start learning topic analysis with a focus on techniques for mining one topic from text.
What's included
10 videos1 reading2 assignments
During this module, you will learn topic analysis in depth, including mixture models and how they work, Expectation-Maximization (EM) algorithm and how it can be used to estimate parameters of a mixture model, the basic topic model, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.
What's included
10 videos2 readings2 assignments1 programming assignment
During this module, you will learn text clustering, including the basic concepts, main clustering techniques, including probabilistic approaches and similarity-based approaches, and how to evaluate text clustering. You will also start learning text categorization, which is related to text clustering, but with pre-defined categories that can be viewed as pre-defining clusters.
What's included
9 videos1 reading2 assignments
During this module, you will continue learning about various methods for text categorization, including multiple methods classified under discriminative classifiers, and you will also learn sentiment analysis and opinion mining, including a detailed introduction to a particular technique for sentiment classification (i.e., ordinal regression).
What's included
7 videos1 reading2 assignments
During this module, you will continue learning about sentiment analysis and opinion mining with a focus on Latent Aspect Rating Analysis (LARA), and you will learn about techniques for joint mining of text and non-text data, including contextual text mining techniques for analyzing topics in text in association with various context information such as time, location, authors, and sources of data. You will also see a summary of the entire course.
What's included
8 videos1 reading2 assignments1 plugin
Instructor
Instructor ratings
4.5 (53 ratings)Offered by
Recommended if you're interested in Data Analysis
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4.5
727 reviews
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Showing 3 of 727
GP
5
Reviewed on Nov 1, 2017
Outstanding mix of theory and practical applications to help understand the theory. Well organized and excellent presentations. Thank you!
WP
4
Reviewed on Aug 22, 2017
Most of the lessons are mathematical formulae in which, in my opinion, I need more real case study/practice to make myself clearly understand on how do those formulae perform.
DO
5
Reviewed on Dec 3, 2022
Prof. Zhai's textbook is well-worth the added investment. His Coursera lectures helped me to "read between the lines."
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