Great course

讲的很不错，受益匪浅。

Excellent!

Thank you

Excellent

excellent

Very good

Awesome!

Great!!

nice

Nice

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E

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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!

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.

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.

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!

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

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.

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.

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.

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.

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

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.