Yonsei - Hands-on Text Mining and Analytics
- Offered byCoursera
Hands-on Text Mining and Analytics at Coursera Overview
Duration | 13 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Hands-on Text Mining and Analytics at Coursera Highlights
- Earn a certificate from the Yonsei university upon completion of course.
- Flexible deadlines according to your schedule.
Hands-on Text Mining and Analytics at Coursera Course details
- 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.
Hands-on Text Mining and Analytics at Coursera Curriculum
Course Logistics and the Text Mining Tool for the Course
1.1 Description of the course including the objectives and outcomes
1.2 Explanations of the y-TextMiner package and the datasets
1.3 How-to-do: workspace installation and setup
1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner1.2.zip)
What is Text Mining?
Text Preprocessing
2.1 Description of possible project ideas
2.2 What is text mining?
2.3 Description of preprocessing techniques
2.4 How-to-do: normalization including tokenization and lemmatization
2.5 How-to-do: N-Grams
Text Preprocessing
Text Analysis Techniques
3.1 Description of stopword removal, stemming, and POS tagging
3.2 Explanations of named entity recognition
3.3 Explanations of dependency parsing
3.4 How-to-do: stopword removal and stemming
3.5 How-to-do: NER and POS Tagging
3.6 How-to-do: constituency and dependency parsing
Stemming and Lemmatization
Named Entity Recognition
Term Weighting and Document Classification
4.1 Explanations of TF*IDF
4.2 Explanations of document classification
4.3 Explanations of sentiment analysis
4.4 How-to-do: computation of tf*idf weighting
4.5 How-to-do: classification with Logistic Regression
Text Classification
TF-IDF
Sentiment Analysis
5.1 Explanations of sentiment analysis with supervised learning
5.2 Explanations of sentiment analysis with unsupervised learning
5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet
5.4 How-to-do: sentiment analysis with CoreNLP
5.5 How-to-do: sentiment analysis with LingPipe
5.6 How-to-do: sentiment analysis with SentiWordNet
Opinion mining and sentiment analysis by Bo Pang and Lillian Lee
Topic Modeling
6.1 Description of Topic Modeling
6.2 Explanations of LDA and DMR
6.3 Description of Topic Modeling with Mallet
6.4 How-to-do: LDA
6.5 How-to-do: DMR
Introduction to Probabilistic Topic Models by David Blei
Hands-on Text Mining and Analytics at Coursera Admission Process
Important Dates
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