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Learner Reviews & Feedback for Text Mining and Analytics by University of Illinois at Urbana-Champaign

619 ratings
137 reviews

About the 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....

Top reviews

Feb 9, 2017

Excellent course, the pipeline they propose to help you understand text mining is quite helpful. It has an important introduction to the most key concepts and techniques for text mining and analytics.

Mar 24, 2018

The content of Text Mining and Analytics is very comprehensive and deep. More practise about how formula works would be better. Quiz could be not tough to be completed after attending every lectures.

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76 - 100 of 136 Reviews for Text Mining and Analytics

By David O

Jul 1, 2018

Great course

By 黄莉婷

Dec 27, 2017


By Florov M

Apr 3, 2020


By Kamlesh C

Aug 23, 2020

Thank you

By Kumar B P

May 8, 2020


By R M

Apr 29, 2020


By MItrajyoti K

Oct 24, 2019

Very good

By Hernan C V

May 4, 2017


By Arefeh Y

Nov 4, 2016


By Swapna.C

Jul 17, 2020


By Mrinal G

May 20, 2019


By Isaiah M

Jan 2, 2018


By Valerie P

Jul 11, 2017


By Deepak S

Aug 11, 2016


By Jennifer K

Jul 5, 2017

Despite the amount of material to cover, this course did a great job of introducing the right amount of detail for various aspects (motivation, algorithms, algorithmic reasoning, evaluation) on topic modelling, text clustering, text categorization, sentiment analysis, aspect sentiment analysis, evaluation of text and non-text data in context, and more. Definitely read the additional resources for the material - it will give you an incredibly in-depth view to what you learned in the lectures and also give you a start on implementing the covered algorithms on your own.

The only thing I missed in this class are assignments for implementing the algorithms in a language other than C++ and in a framework other than MeTA. It would make sense to provide this opportunity in additional, commonly-used data-science languages such as Python!

By Milan M

Sep 14, 2016

This is an excellent course that captures many different text mining techniques. It requires some math knowledge in numerical analysis and probability in order to understand the concepts.

I gave 4 star rating due to 2 problems during the course:

1) Lack of examples along the formulas and principles. There are some, but many concepts could be adopted much faster if examples were introduced right along with them.

2) The optional programming exercises are easy to complete, but the environment is very confusing to set it up.

By Gonzalo d l T A

May 10, 2017

A really interesting course which covers theoretically most of the text mining techniques. I missed having more practical exercise, which could help to deeply understand the lectures. Setting up the environment for the development task is a little bit complicated, it might be interesting to provide a virtual machine with all the software and correct versions required. Even though, I would recommend this course if you are interested on the topic.

By Arkadiusz R

Jul 9, 2017

Very good course with a lot of essential information about problems correlated with text understanding. It give me general look for text mining topic. Some lectures give only overall information about text analysis problem, but it still gives me an opportunity to learn about these listed topics to resolve relevant problems. I recommend this course anyone!

By fakhriabbas

Oct 2, 2016

the course is very helpful in giving the overall flavor of text mining and analytics. I would recommend to reduce the number of math work and focus on the conceptual level along with more application that could be used. For the math part, adding optional videos for more details about math will be very useful and helpful

By Ahmed M S

Jan 12, 2020

This is an excellent foundational course about text mining. It provides a very solid theoretical foundations and concepts about the subject. The only thing that felt missing, is giving more numerical examples during the video sessions to ease understanding the formulas.

By Alex D T

Jul 22, 2017

Professor Cheng has a deep knowledge of the subject and presents a diverse topic in a very condensed set of courses. Material is well presented, but some of the quizzes and slides need to be better organized.

By Akarapat C

Feb 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.

By Watana P

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.

By Aravindh

Apr 19, 2017

The content is really good but the course has too much theory. Mixing it with some practical programming assignments would have been very nice

By Ian W

Aug 10, 2018

In-depth description on the algorithms.

Personally I suggest finish the quiz of the nth week after finishing all the video of (n+1)th week.