About this course: 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. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.

Created by: University of Illinois at Urbana-Champaign

Taught by: ChengXiang Zhai, Professor
Department of Computer Science

Basic Info	Course 3 of 6 in the Data Mining Specialization.
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.4 stars Average User Rating 4.4See what learners said

Syllabus

WEEK 1

Orientation

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.

2 videos, 5 readings, 1 practice quiz

Graded: Orientation Quiz

Week 1

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...

9 videos, 1 reading, 1 practice quiz

Graded: Week 1 Quiz

WEEK 2

Week 2

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 fr...

10 videos, 1 reading, 1 practice quiz

Graded: Week 2 Quiz

WEEK 3

Week 3

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 Semant...

10 videos, 2 readings, 1 practice quiz

Graded: Quiz: Week 3 Quiz

Graded: Programming Assignment

WEEK 4

Week 4

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 categorizat...

9 videos, 1 reading, 1 practice quiz

Graded: Week 4 Quiz

WEEK 5

Week 5

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 introducti...

7 videos, 1 reading, 1 practice quiz

Graded: Week 5 Quiz

WEEK 6

Week 6

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 te...

8 videos, 1 reading, 1 practice quiz

Graded: Week 6 Quiz

FAQs

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Coursework

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Creators

University of Illinois at Urbana-Champaign

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.

Pricing

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Ratings and Reviews

Rated 4.4 out of 5 of 142 ratings

My favorite course and my favorite instructor. Highly informative. I wish i want to be a real student of the instructor.

Outstanding course, I love the way of teaching

The content was very useful, and the preparation of the course denoted much care and preparation by the teacher. I would love to see some modern topics like word embeddings covered in the course!

Useful as introductory course on NLP. Recommended for anyone who wants to begin on this discipline.

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Text Mining and Analytics

Text Mining and Analytics

Text Mining and Analytics