This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.
Offered By
Yonsei University
About this Course
5,520 recent views
Learners taking this Course are
- Data Scientists
- Data Analysts
- Scientists
- Business Analysts
- Data Engineers
Syllabus - What you will learn from this course
Week
13 hours to complete
Course Logistics and the Text Mining Tool for the Course
4 videos (Total 53 min), 1 reading, 1 quiz
4 videos
1.2 Explanations of the y-TextMiner package and the datasets9m
1.3 How-to-do: workspace installation and setup15m
1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner1.2.zip)13m
1 reading
What is Text Mining?10m
Week
23 hours to complete
Text Preprocessing
5 videos (Total 67 min), 1 reading, 1 quiz
5 videos
2.2 What is text mining?10m
2.3 Description of preprocessing techniques11m
2.4 How-to-do: normalization including tokenization and lemmatization20m
2.5 How-to-do: N-Grams14m
1 reading
Text Preprocessing10m
Week
33 hours to complete
Text Analysis Techniques
6 videos (Total 62 min), 2 readings, 1 quiz
6 videos
3.2 Explanations of named entity recognition11m
3.3 Explanations of dependency parsing8m
3.4 How-to-do: stopword removal and stemming14m
3.5 How-to-do: NER and POS Tagging6m
3.6 How-to-do: constituency and dependency parsing9m
2 readings
Stemming and Lemmatization10m
Named Entity Recognition10m
Week
43 hours to complete
Term Weighting and Document Classification
5 videos (Total 52 min), 2 readings, 1 quiz
5 videos
4.2 Explanations of document classification11m
4.3 Explanations of sentiment analysis9m
4.4 How-to-do: computation of tf*idf weighting10m
4.5 How-to-do: classification with Logistic Regression11m
2 readings
Text Classification10m
TF-IDF10m
4.0
8 ReviewsTop reviews from Hands-on Text Mining and Analytics
Excellent theory and hands-on lab codes. It'd be great if you could also cover how-to in other relevant programming languages using R or Python.
Instructor
Min Song
ProfessorLibrary & Information Technology
About Yonsei University
Yonsei University was established in 1885 and is the oldest private university in Korea.
Yonsei’s main campus is situated minutes away from the economic, political, and cultural centers of Seoul’s metropolitan downtown. Yonsei has 3,500 eminent faculty members who are conducting cutting-edge research across all academic disciplines. There are 18 graduate schools, 22 colleges and 133 subsidiary institutions hosting a selective pool of students from around the world.
Yonsei is proud of its history and reputation as a leading institution of higher education and research in Asia.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, 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.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.