Learner Reviews & Feedback for Text Mining and Analytics by University of Illinois Urbana-Champaign
About the Course
Top reviews
DC
Mar 25, 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.
JS
Jun 7, 2017
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!
76 - 100 of 148 Reviews for Text Mining and Analytics
By Gourav A
•Oct 26, 2018
Excellent course.
By RAM K
•Aug 23, 2020
excellent course
By aditya r
•Dec 13, 2020
its nice course
By Raja R
•Jan 22, 2021
Great Course!
By VIKAS M
•Dec 16, 2020
fun learning
By Manikant R
•Jun 21, 2020
great course
By David O
•Jul 1, 2018
Great course
By KATKURI G K R
•Aug 31, 2023
good course
By 黄莉婷
•Dec 27, 2017
讲的很不错，受益匪浅。
By Florov M
•Apr 3, 2020
Excellent!
By Kamlesh C
•Aug 23, 2020
Thank you
By Kumar B P
•May 8, 2020
Excellent
By Assoc.Prof., C V T C
•Apr 29, 2020
excellent
By MItrajyoti K
•Oct 24, 2019
Very good
By 2K18/SE/129 V K
•May 9, 2022
good one
By Hernán C V
•May 4, 2017
Awesome!
By Arefeh Y
•Nov 5, 2016
Great!!
By kalashri
•Aug 24, 2023
great
By Нұрсұлтан У
•Feb 6, 2025
++++
By Swapna.C
•Jul 17, 2020
nice
By Mrinal G
•May 20, 2019
Nice
By Isaiah M
•Jan 2, 2018
T
By Valerie P
•Jul 11, 2017
E
By Deepak S
•Aug 11, 2016
E
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!