This course is part of the Data Mining Specialization

4.5
stars
698 ratings

ChengXiang Zhai

66,731 already enrolled

Offered By

Data Mining SpecializationUniversity of Illinois at Urbana-Champaign

About this Course

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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.

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Course 3 of 6 in the

Data Mining Specialization

Approx. 33 hours to complete

English

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Skills you will gain

Data Clustering Algorithms
Text Mining
Probabilistic Models
Sentiment Analysis

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 6 in the

Data Mining Specialization

Approx. 33 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.55/5 (50 Ratings)

ChengXiang Zhai

Professor

Department of Computer Science

95,812 Learners

4 Courses

Offered by

University of Illinois at Urbana-Champaign

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Syllabus - What you will learn from this course

Content Rating92%(2,977 ratings)

Week

Week 1

2 hours to complete

Orientation

2 hours to complete

2 videos (Total 15 min), 5 readings, 2 quizzes

4 hours to complete

Week 1

4 hours to complete

9 videos (Total 109 min), 1 reading, 2 quizzes

9 videos

1.1 Overview Text Mining and Analytics: Part 111m

1.2 Overview Text Mining and Analytics: Part 211m

1.3 Natural Language Content Analysis: Part 112m

1.4 Natural Language Content Analysis: Part 24m

1.5 Text Representation: Part 110m

1.6 Text Representation: Part 29m

1.7 Word Association Mining and Analysis15m

1.8 Paradigmatic Relation Discovery Part 114m

1.9 Paradigmatic Relation Discovery Part 217m

1 reading

Week 1 Overview10m

2 practice exercises

Week 1 Practice Quiz1h

Week 1 Quiz1h

Week

Week 2

4 hours to complete

Week 2

4 hours to complete

10 videos (Total 116 min), 1 reading, 2 quizzes

10 videos

2.1 Syntagmatic Relation Discovery: Entropy11m

2.2 Syntagmatic Relation Discovery: Conditional Entropy11m

2.3 Syntagmatic Relation Discovery: Mutual Information: Part 113m

2.4 Syntagmatic Relation Discovery: Mutual Information: Part 29m

2.5 Topic Mining and Analysis: Motivation and Task Definition7m

2.6 Topic Mining and Analysis: Term as Topic11m

2.7 Topic Mining and Analysis: Probabilistic Topic Models14m

2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 110m

2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 213m

2.10 Probabilistic Topic Models: Mining One Topic12m

1 reading

Week 2 Overview10m

2 practice exercises

Week 2 Practice Quiz1h

Week 2 Quiz1h

Week

Week 3

10 hours to complete

Week 3

10 hours to complete

10 videos (Total 103 min), 2 readings, 3 quizzes

10 videos

3.1 Probabilistic Topic Models: Mixture of Unigram Language Models12m

3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110m

3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28m

3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111m

3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 210m

3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36m

3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110m

3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210m

3.9 Latent Dirichlet Allocation (LDA): Part 110m

3.10 Latent Dirichlet Allocation (LDA): Part 212m

2 readings

Week 3 Overview10m

Programming Assignments Overview10m

2 practice exercises

Week 3 Practice Quiz1h

Quiz: Week 3 Quiz1h

Week

Week 4

5 hours to complete

Week 4

5 hours to complete

9 videos (Total 141 min), 1 reading, 2 quizzes

9 videos

4.1 Text Clustering: Motivation15m

4.2 Text Clustering: Generative Probabilistic Models Part 116m

4.3 Text Clustering: Generative Probabilistic Models Part 28m

4.4 Text Clustering: Generative Probabilistic Models Part 314m

4.5 Text Clustering: Similarity-based Approaches17m

4.6 Text Clustering: Evaluation10m

4.7 Text Categorization: Motivation14m

4.8 Text Categorization: Methods11m

4.9 Text Categorization: Generative Probabilistic Models31m

1 reading

Week 4 Overview10m

2 practice exercises

Week 4 Practice Quiz1h

Week 4 Quiz1h

Week

Week 5

4 hours to complete

Week 5

4 hours to complete

7 videos (Total 121 min), 1 reading, 2 quizzes

7 videos

5.1 Text Categorization: Discriminative Classifier Part 120m

5.2 Text Categorization: Discriminative Classifier Part 231m

5.3 Text Categorization: Evaluation Part 114m

5.4 Text Categorization: Evaluation Part 210m

5.5 Opinion Mining and Sentiment Analysis: Motivation17m

5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification11m

5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression13m

1 reading

Week 5 Overview10m

2 practice exercises

Week 5 Practice Quiz1h

Week 5 Quiz1h

Week

Week 6

4 hours to complete

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 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.

4 hours to complete

8 videos (Total 120 min), 1 reading, 2 quizzes

8 videos

6.1 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 115m

6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 214m

6.3 Text-Based Prediction12m

6.4 Contextual Text Mining: Motivation6m

6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis17m

6.6 Contextual Text Mining: Mining Topics with Social Network Context14m

6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision19m

6.8 Course Summary18m

1 reading

Week 6 Overview10m

2 practice exercises

Week 6 Practice Quiz1h

Week 6 Quiz1h

Reviews

4.5

144 reviews

5 stars
68.09%
4 stars
20.45%
3 stars
7.86%
2 stars
1.85%
1 star
1.71%

TOP REVIEWS FROM TEXT MINING AND ANALYTICS

by SGFeb 24, 2017

Very good course thank you

I wish we could have use case applications with high level tools such as R

Thanks a lot again !

by DODec 3, 2022

Prof. Zhai's textbook is well-worth the added investment. His Coursera lectures helped me to "read between the lines."

by LVJul 26, 2020

Well taught course, would have preferred if went further into topics like opinion mining (even if we had optional lectures or assignments). However, definitely a fun course to learn a lot.

by ACFeb 11, 2017

This is a very good course. I think it provides a very good foundation of text mining and analytics like PLSA and LDA. More advanced research discussed in the last lecture is also very interesting.

View all reviews

About the Data Mining Specialization

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Text Mining and Analytics

About this Course

Skills you will gain