关于此课程： 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.
制作方： 延世大学
教学方： Min Song, Professor
Library & Information Technology
| 级别 | Intermediate |
| 语言 | English, 字幕： Spanish, Chinese (Simplified) |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
Course Logistics and the Text Mining Tool for the Course
4 视频, 1 阅读材料
- 视频: 1.2 Explanations of the y-TextMiner package and the datasets
- 视频: 1.3 How-to-do: workspace installation and setup
- 视频: 1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner.zip)
- Reading: What is Text Mining?
- Peer Review: y-TextMiner installation and a simple Java program
第 2 周
Text Preprocessing
5 视频, 1 阅读材料
- 视频: 2.2 What is text mining?
- 视频: 2.3 Description of preprocessing techniques
- 视频: 2.4 How-to-do: normalization including tokenization and lemmatization
- 视频: 2.5 How-to-do: N-Grams
- Reading: Text Preprocessing
- Peer Review: Preprocessing Practice
第 3 周
Text Analysis Techniques
6 视频, 2 阅读材料
- 视频: 3.2 Explanations of named entity recognition
- 视频: 3.3 Explanations of dependency parsing
- 视频: 3.4 How-to-do: stopword removal and stemming
- 视频: 3.5 How-to-do: NER and POS Tagging
- 视频: 3.6 How-to-do: constituency and dependency parsing
- Reading: Stemming and Lemmatization
- Reading: Named Entity Recognition
已评分: Text Analysis Practice
第 4 周
Term Weighting and Document Classification
5 视频, 2 阅读材料
- 视频: 4.2 Explanations of document classification
- 视频: 4.3 Explanations of sentiment analysis
- 视频: 4.4 How-to-do: computation of tf*idf weighting
- 视频: 4.5 How-to-do: classification with Logistic Regression
- Reading: Text Classification
- Reading: TF-IDF
已评分: Document Classification Practice
第 5 周
Sentiment Analysis
6 视频, 1 阅读材料
- 视频: 5.2 Explanations of sentiment analysis with unsupervised learning
- 视频: 5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet
- 视频: 5.4 How-to-do: sentiment analysis with CoreNLP
- 视频: 5.5 How-to-do: sentiment analysis with LingPipe
- 视频: 5.6 How-to-do: sentiment analysis with SentiWordNet
- Reading: Opinion mining and sentiment analysis by Bo Pang and Lillian Lee
已评分: Sentiment Analysis Practice
第 6 周
Topic Modeling
5 视频, 1 阅读材料
- 视频: 6.2 Explanations of LDA and DMR
- 视频: 6.3 Description of Topic Modeling with Mallet
- 视频: 6.4 How-to-do: LDA
- 视频: 6.5 How-to-do: DMR
- Reading: Introduction to Probabilistic Topic Models by David Blei
已评分: Topic Modeling Practice
常见问题解答
运作方式
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
制作方
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.
评分和审阅
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.
Excellent course !!
Thanks