Hands-on Text Mining and Analytics

Hands-on Text Mining and Analytics

Instructor: Min Song

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Taught in English

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There are 6 modules in this course

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.

What's included

4 videos1 reading1 peer review

4 videosTotal 53 minutes

1.1 Description of the course including the objectives and outcomes14 minutesPreview module
1.2 Explanations of the y-TextMiner package and the datasets9 minutes
1.3 How-to-do: workspace installation and setup15 minutes
1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner1.2.zip)13 minutes

1 readingTotal 10 minutes

What is Text Mining?10 minutes

1 peer reviewTotal 60 minutes

y-TextMiner installation and a simple Java program60 minutes

What's included

5 videos1 reading1 peer review

5 videosTotal 67 minutes

2.1 Description of possible project ideas10 minutesPreview module
2.2 What is text mining?10 minutes
2.3 Description of preprocessing techniques11 minutes
2.4 How-to-do: normalization including tokenization and lemmatization20 minutes
2.5 How-to-do: N-Grams14 minutes

1 readingTotal 10 minutes

Text Preprocessing10 minutes

1 peer reviewTotal 60 minutes

Preprocessing Practice60 minutes

What's included

6 videos2 readings1 peer review

6 videosTotal 62 minutes

3.1 Description of stopword removal, stemming, and POS tagging12 minutesPreview module
3.2 Explanations of named entity recognition11 minutes
3.3 Explanations of dependency parsing8 minutes
3.4 How-to-do: stopword removal and stemming14 minutes
3.5 How-to-do: NER and POS Tagging6 minutes
3.6 How-to-do: constituency and dependency parsing9 minutes

2 readingsTotal 20 minutes

Stemming and Lemmatization10 minutes
Named Entity Recognition10 minutes

1 peer reviewTotal 60 minutes

Text Analysis Practice60 minutes

What's included

5 videos2 readings1 peer review

5 videosTotal 51 minutes

4.1 Explanations of TF*IDF9 minutesPreview module
4.2 Explanations of document classification11 minutes
4.3 Explanations of sentiment analysis9 minutes
4.4 How-to-do: computation of tf*idf weighting10 minutes
4.5 How-to-do: classification with Logistic Regression11 minutes

2 readingsTotal 20 minutes

Text Classification10 minutes
TF-IDF10 minutes

1 peer reviewTotal 60 minutes

Document Classification Practice60 minutes

What's included

6 videos1 reading1 peer review

6 videosTotal 59 minutes

5.1 Explanations of sentiment analysis with supervised learning9 minutesPreview module
5.2 Explanations of sentiment analysis with unsupervised learning10 minutes
5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet10 minutes
5.4 How-to-do: sentiment analysis with CoreNLP8 minutes
5.5 How-to-do: sentiment analysis with LingPipe9 minutes
5.6 How-to-do: sentiment analysis with SentiWordNet10 minutes