Hands-on Text Mining and Analytics
Master practical text mining techniques using Java-based tools. Learn preprocessing, sentiment analysis, and topic modeling with real-world datasets.
Master practical text mining techniques using Java-based tools. Learn preprocessing, sentiment analysis, and topic modeling with real-world datasets.
This comprehensive course provides hands-on training in text mining and analytics using real-world datasets and a specialized Java toolkit. Students learn core techniques including text preprocessing, sentiment analysis, and topic modeling through practical lab sessions using the y-TextMiner toolkit. The course combines theoretical concepts with extensive hands-on practice, enabling learners to develop real-world text mining applications and gain practical data science skills.
3.9
(40 ratings)
15,699 already enrolled
Instructors:
English
English (Original), Deutsch (Auto), हिन्दी (ऑटो), 18 more
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What you'll learn
Master core text preprocessing techniques and implementations
Develop practical skills in sentiment analysis using multiple approaches
Implement document classification and term weighting methods
Gain hands-on experience with topic modeling algorithms
Learn to use professional text mining tools and libraries
Apply text mining techniques to real-world datasets
Skills you'll gain
This course includes:
347 Minutes PreRecorded video
6 peer reviews
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This practical course focuses on text mining and analytics implementation using Java-based tools. The curriculum covers essential text mining techniques from preprocessing to advanced analysis. Students learn through hands-on experience with the y-TextMiner toolkit, working on real datasets to master text preprocessing, sentiment analysis, document classification, and topic modeling. The course emphasizes practical application and implementation of text mining concepts through guided lab sessions and peer-reviewed assignments.
Course Logistics and the Text Mining Tool for the Course
Module 1 · 2 Hours to complete
Text Analysis Techniques
Module 2 · 2 Hours to complete
Term Weighting and Document Classification
Module 3 · 2 Hours to complete
Text Preprocessing
Module 4 · 2 Hours to complete
Sentiment Analysis
Module 5 · 2 Hours to complete
Topic Modeling
Module 6 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Professor
Min Song is an Underwood Distinguished Professor in the Department of Library and Information Science at Yonsei University. Previously, he was an Associate Professor at New Jersey Institute of Technology, focusing on knowledge discovery from large natural language datasets. His research interests include biomedical text mining, social media mining, and informetrics, and he has published over 150 journal and conference papers.
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3.9 course rating
40 ratings
Frequently asked questions
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