About 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. 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.
Created by: Yonsei University
Taught by: Min Song, Professor
Library & Information Technology
| Level | Intermediate |
| Language | English, Subtitles: Spanish, Chinese (Simplified) |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Course Logistics and the Text Mining Tool for the Course
4 videos, 1 reading
- Video: 1.1 Description of the course including the objectives and outcomes
- Video: 1.2 Explanations of the y-TextMiner package and the datasets
- Video: 1.3 How-to-do: workspace installation and setup
- Video: 1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner.zip)
- Lecture: What is Text Mining?
- Examen par les pairs: y-TextMiner installation and a simple Java program
WEEK 2
Text Preprocessing
5 videos, 1 reading
- Video: 2.1 Description of possible project ideas
- Video: 2.2 What is text mining?
- Video: 2.3 Description of preprocessing techniques
- Video: 2.4 How-to-do: normalization including tokenization and lemmatization
- Video: 2.5 How-to-do: N-Grams
- Lecture: Text Preprocessing
- Examen par les pairs: Preprocessing Practice
WEEK 3
Text Analysis Techniques
6 videos, 2 readings
- Video: 3.1 Description of stopword removal, stemming, and POS tagging
- Video: 3.2 Explanations of named entity recognition
- Video: 3.3 Explanations of dependency parsing
- Video: 3.4 How-to-do: stopword removal and stemming
- Video: 3.5 How-to-do: NER and POS Tagging
- Video: 3.6 How-to-do: constituency and dependency parsing
- Lecture: Stemming and Lemmatization
- Lecture: Named Entity Recognition
Graded: Text Analysis Practice
WEEK 4
Term Weighting and Document Classification
5 videos, 2 readings
- Video: 4.1 Explanations of TF*IDF
- Video: 4.2 Explanations of document classification
- Video: 4.3 Explanations of sentiment analysis
- Video: 4.4 How-to-do: computation of tf*idf weighting
- Video: 4.5 How-to-do: classification with Logistic Regression
- Lecture: Text Classification
- Lecture: TF-IDF
Graded: Document Classification Practice
WEEK 5
Sentiment Analysis
6 videos, 1 reading
- Video: 5.1 Explanations of sentiment analysis with supervised learning
- Video: 5.2 Explanations of sentiment analysis with unsupervised learning
- Video: 5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet
- Video: 5.4 How-to-do: sentiment analysis with CoreNLP
- Video: 5.5 How-to-do: sentiment analysis with LingPipe
- Video: 5.6 How-to-do: sentiment analysis with SentiWordNet
- Lecture: Opinion mining and sentiment analysis by Bo Pang and Lillian Lee
Graded: Sentiment Analysis Practice
WEEK 6
Topic Modeling
5 videos, 1 reading
- Video: 6.1 Description of Topic Modeling
- Video: 6.2 Explanations of LDA and DMR
- Video: 6.3 Description of Topic Modeling with Mallet
- Video: 6.4 How-to-do: LDA
- Video: 6.5 How-to-do: DMR
- Lecture: Introduction to Probabilistic Topic Models by David Blei
Graded: Topic Modeling Practice
FAQs
How It Works
Travail en cours
Chaque cours fonctionne comme un manuel interactif en proposant des vidéos préenregistrées, des quiz et des projets.
Aide de la part de vos pairs
Connectez-vous à des milliers d'autres étudiants et débattez sur des idées, discutez le contenu du cours et obtenez de l'aide pour en maîtriser les concepts.
Certificats
Obtenez une reconnaissance officielle pour votre travail et partagez votre réussite avec vos amis, vos collègues et vos employeurs.
Creators
Yonsei University
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.
Ratings and Reviews
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 !!
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