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.
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 3 小时
Course Logistics and the Text Mining Tool for the Course
4 个视频 （总计 53 分钟）, 1 个阅读材料, 1 个测验
4 个视频
1.2 Explanations of the y-TextMiner package and the datasets9分钟
1.3 How-to-do: workspace installation and setup15分钟
1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner1.2.zip)13分钟
1 个阅读材料
What is Text Mining?10分钟
周
2完成时间为 3 小时
Text Preprocessing
5 个视频 （总计 67 分钟）, 1 个阅读材料, 1 个测验
5 个视频
2.2 What is text mining?10分钟
2.3 Description of preprocessing techniques11分钟
2.4 How-to-do: normalization including tokenization and lemmatization20分钟
2.5 How-to-do: N-Grams14分钟
1 个阅读材料
Text Preprocessing10分钟
周
3完成时间为 3 小时
Text Analysis Techniques
6 个视频 （总计 62 分钟）, 2 个阅读材料, 1 个测验
6 个视频
3.2 Explanations of named entity recognition11分钟
3.3 Explanations of dependency parsing8分钟
3.4 How-to-do: stopword removal and stemming14分钟
3.5 How-to-do: NER and POS Tagging6分钟
3.6 How-to-do: constituency and dependency parsing9分钟
2 个阅读材料
Stemming and Lemmatization10分钟
Named Entity Recognition10分钟
周
4完成时间为 3 小时
Term Weighting and Document Classification
5 个视频 （总计 52 分钟）, 2 个阅读材料, 1 个测验
5 个视频
4.2 Explanations of document classification11分钟
4.3 Explanations of sentiment analysis9分钟
4.4 How-to-do: computation of tf*idf weighting10分钟
4.5 How-to-do: classification with Logistic Regression11分钟
2 个阅读材料
Text Classification10分钟
TF-IDF10分钟
周
5完成时间为 3 小时
Sentiment Analysis
6 个视频 （总计 59 分钟）, 1 个阅读材料, 1 个测验
6 个视频
5.2 Explanations of sentiment analysis with unsupervised learning10分钟
5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet10分钟
5.4 How-to-do: sentiment analysis with CoreNLP8分钟
5.5 How-to-do: sentiment analysis with LingPipe9分钟
5.6 How-to-do: sentiment analysis with SentiWordNet10分钟
1 个阅读材料
Opinion mining and sentiment analysis by Bo Pang and Lillian Lee10分钟
周
6完成时间为 3 小时
Topic Modeling
5 个视频 （总计 55 分钟）, 1 个阅读材料, 1 个测验
5 个视频
6.2 Explanations of LDA and DMR10分钟
6.3 Description of Topic Modeling with Mallet13分钟
6.4 How-to-do: LDA11分钟
6.5 How-to-do: DMR11分钟
1 个阅读材料
Introduction to Probabilistic Topic Models by David Blei10分钟
4.0
8 个审阅热门审阅
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.
讲师
Min Song
ProfessorLibrary & Information Technology
关于 延世大学
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.
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