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.
Offered By
Hands-on Text Mining and Analytics
Yonsei UniversityAbout this Course
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Intermediate Level
Approx. 13 hours to complete
English
Subtitles: French, Portuguese (Brazilian), Chinese (Simplified), Russian, English, Spanish
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 13 hours to complete
English
Subtitles: French, Portuguese (Brazilian), Chinese (Simplified), Russian, English, Spanish
Offered by
Yonsei University
Yonsei University was established in 1885 and is the oldest private university in Korea.
Syllabus - What you will learn from this course
2 hours to complete
Course Logistics and the Text Mining Tool for the Course
2 hours to complete
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
2 hours to complete
Text Preprocessing
2 hours to complete
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
2 hours to complete
Text Analysis Techniques
2 hours to complete
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
2 hours to complete
Term Weighting and Document Classification
2 hours to complete
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
Reviews
TOP REVIEWS FROM HANDS-ON TEXT MINING AND ANALYTICS
by KAMay 30, 2017
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.
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