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.
提供方
延世大学
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 3 小時
Course Logistics and the Text Mining Tool for the Course
4 個視頻 （總計 53 分鐘）, 1 個閱讀材料, 1 個測驗
4 個視頻
1.2 Explanations of the y-TextMiner package and the datasets9分鐘
1.3 How-to-do: workspace installation and setup15分鐘
1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner1.2.zip)13分鐘
1 個閱讀材料
What is Text Mining?10分鐘
週
2完成時間為 3 小時
Text Preprocessing
5 個視頻 （總計 67 分鐘）, 1 個閱讀材料, 1 個測驗
5 個視頻
2.2 What is text mining?10分鐘
2.3 Description of preprocessing techniques11分鐘
2.4 How-to-do: normalization including tokenization and lemmatization20分鐘
2.5 How-to-do: N-Grams14分鐘
1 個閱讀材料
Text Preprocessing10分鐘
週
3完成時間為 3 小時
Text Analysis Techniques
6 個視頻 （總計 62 分鐘）, 2 個閱讀材料, 1 個測驗
6 個視頻
3.2 Explanations of named entity recognition11分鐘
3.3 Explanations of dependency parsing8分鐘
3.4 How-to-do: stopword removal and stemming14分鐘
3.5 How-to-do: NER and POS Tagging6分鐘
3.6 How-to-do: constituency and dependency parsing9分鐘
2 個閱讀材料
Stemming and Lemmatization10分鐘
Named Entity Recognition10分鐘
週
4完成時間為 3 小時
Term Weighting and Document Classification
5 個視頻 （總計 52 分鐘）, 2 個閱讀材料, 1 個測驗
5 個視頻
4.2 Explanations of document classification11分鐘
4.3 Explanations of sentiment analysis9分鐘
4.4 How-to-do: computation of tf*idf weighting10分鐘
4.5 How-to-do: classification with Logistic Regression11分鐘
2 個閱讀材料
Text Classification10分鐘
TF-IDF10分鐘
週
5完成時間為 3 小時
Sentiment Analysis
6 個視頻 （總計 59 分鐘）, 1 個閱讀材料, 1 個測驗
6 個視頻
5.2 Explanations of sentiment analysis with unsupervised learning10分鐘
5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet10分鐘
5.4 How-to-do: sentiment analysis with CoreNLP8分鐘
5.5 How-to-do: sentiment analysis with LingPipe9分鐘
5.6 How-to-do: sentiment analysis with SentiWordNet10分鐘
1 個閱讀材料
Opinion mining and sentiment analysis by Bo Pang and Lillian Lee10分鐘
週
6完成時間為 3 小時
Topic Modeling
5 個視頻 （總計 55 分鐘）, 1 個閱讀材料, 1 個測驗
5 個視頻
6.2 Explanations of LDA and DMR10分鐘
6.3 Description of Topic Modeling with Mallet13分鐘
6.4 How-to-do: LDA11分鐘
6.5 How-to-do: DMR11分鐘
1 個閱讀材料
Introduction to Probabilistic Topic Models by David Blei10分鐘
4.0
8 個審閱熱門審閱
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.
講師
Min Song
ProfessorLibrary & Information Technology
關於 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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