Об этом курсе: 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.

Автор: Yonsei University

Преподаватели: Min Song, Professor
Library & Information Technology

Уровень	Учащийся среднего уровня
Язык	English, Субтитры: Spanish, Chinese (Simplified)
Как пройти курс	Чтобы пройти курс, выполните все оцениваемые задания.
Оценки пользователей	4.1 звезды Средняя оценка пользователей: 4.1Посмотрите, что пишут учащиеся

Программа курса

НЕДЕЛЯ 1

Course Logistics and the Text Mining Tool for the Course

4 видео, 1 материал для самостоятельного изучения

Видео: 1.2 Explanations of the y-TextMiner package and the datasets
Видео: 1.3 How-to-do: workspace installation and setup
Видео: 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

НЕДЕЛЯ 2

Text Preprocessing

5 видео, 1 материал для самостоятельного изучения

Видео: 2.2 What is text mining?
Видео: 2.3 Description of preprocessing techniques
Видео: 2.4 How-to-do: normalization including tokenization and lemmatization
Видео: 2.5 How-to-do: N-Grams
Reading: Text Preprocessing
Peer Review: Preprocessing Practice

НЕДЕЛЯ 3

Text Analysis Techniques

6 видео, 2 материала для самостоятельного изучения

Видео: 3.2 Explanations of named entity recognition
Видео: 3.3 Explanations of dependency parsing
Видео: 3.4 How-to-do: stopword removal and stemming
Видео: 3.5 How-to-do: NER and POS Tagging
Видео: 3.6 How-to-do: constituency and dependency parsing
Reading: Stemming and Lemmatization
Reading: Named Entity Recognition

Оцениваемый: Text Analysis Practice

НЕДЕЛЯ 4

Term Weighting and Document Classification

5 видео, 2 материала для самостоятельного изучения

Видео: 4.2 Explanations of document classification
Видео: 4.3 Explanations of sentiment analysis
Видео: 4.4 How-to-do: computation of tf*idf weighting
Видео: 4.5 How-to-do: classification with Logistic Regression
Reading: Text Classification
Reading: TF-IDF

Оцениваемый: Document Classification Practice

НЕДЕЛЯ 5

Sentiment Analysis

6 видео, 1 материал для самостоятельного изучения

Видео: 5.2 Explanations of sentiment analysis with unsupervised learning
Видео: 5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet
Видео: 5.4 How-to-do: sentiment analysis with CoreNLP
Видео: 5.5 How-to-do: sentiment analysis with LingPipe
Видео: 5.6 How-to-do: sentiment analysis with SentiWordNet
Reading: Opinion mining and sentiment analysis by Bo Pang and Lillian Lee

Оцениваемый: Sentiment Analysis Practice

НЕДЕЛЯ 6

Topic Modeling

5 видео, 1 материал для самостоятельного изучения

Видео: 6.2 Explanations of LDA and DMR
Видео: 6.3 Description of Topic Modeling with Mallet
Видео: 6.4 How-to-do: LDA
Видео: 6.5 How-to-do: DMR
Reading: Introduction to Probabilistic Topic Models by David Blei

Оцениваемый: Topic Modeling Practice

Часто задаваемые вопросы

Когда я получу доступ к лекциям и заданиям?

Что делать, если мне потребуется больше времени на прохождение курса?

Что я получу, оплатив этот курс?

Могу я пройти этот курс бесплатно?

Какие правила возврата средств?

Можно ли получить финансовую помощь?

Как это работает

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.

Авторы

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.

Рейтинги и отзывы

Оценка 4.1 из 5 по 35 отзывам