Об этом курсе: This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.
Автор: Иллинойсский университет в Урбане-Шампейне
Преподаватели: ChengXiang Zhai, Professor
Department of Computer Science
| Основные сведения | Course 3 of 6 in the Data Mining Specialization |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
2 видео, 5 материала для самостоятельного изучения, 1 тренировочный тест
- Reading: Welcome to Text Mining and Analytics!
- Reading: Syllabus
- Reading: About the Discussion Forums
- Reading: Updating your Profile
- Reading: Social Media
- Видео: Course Prerequisites & Completion
- Practice Quiz: Pre-Quiz
Оцениваемый: Orientation Quiz
Week 1
During this module, you will learn the overall course design, an overview of natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications, and word association mining with a particular focus on mining one of the two basic forms of word associations (i.e., paradigmatic relations).
9 видео, 1 материал для самостоятельного изучения, 1 тренировочный тест
- Reading: Week 1 Overview
- Видео: 1.2 Overview Text Mining and Analytics: Part 2
- Видео: 1.3 Natural Language Content Analysis: Part 1
- Видео: 1.4 Natural Language Content Analysis: Part 2
- Видео: 1.5 Text Representation: Part 1
- Видео: 1.6 Text Representation: Part 2
- Видео: 1.7 Word Association Mining and Analysis
- Видео: 1.8 Paradigmatic Relation Discovery Part 1
- Видео: 1.9 Paradigmatic Relation Discovery Part 2
- Practice Quiz: Week 1 Practice Quiz
Оцениваемый: Week 1 Quiz
НЕДЕЛЯ 2
Week 2
During this module, you will learn more about word association mining with a particular focus on mining the other basic form of word association (i.e., syntagmatic relations), and start learning topic analysis with a focus on techniques for mining one topic from text.
10 видео, 1 материал для самостоятельного изучения, 1 тренировочный тест
- Reading: Week 2 Overview
- Видео: 2.2 Syntagmatic Relation Discovery: Conditional Entropy
- Видео: 2.3 Syntagmatic Relation Discovery: Mutual Information: Part 1
- Видео: 2.4 Syntagmatic Relation Discovery: Mutual Information: Part 2
- Видео: 2.5 Topic Mining and Analysis: Motivation and Task Definition
- Видео: 2.6 Topic Mining and Analysis: Term as Topic
- Видео: 2.7 Topic Mining and Analysis: Probabilistic Topic Models
- Видео: 2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 1
- Видео: 2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 2
- Видео: 2.10 Probabilistic Topic Models: Mining One Topic
- Practice Quiz: Week 2 Practice Quiz
Оцениваемый: Week 2 Quiz
НЕДЕЛЯ 3
Week 3
During this module, you will learn topic analysis in depth, including mixture models and how they work, Expectation-Maximization (EM) algorithm and how it can be used to estimate parameters of a mixture model, the basic topic model, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.
10 видео, 2 материала для самостоятельного изучения, 1 тренировочный тест
- Reading: Week 3 Overview
- Видео: 3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 1
- Видео: 3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 2
- Видео: 3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 1
- Видео: 3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 2
- Видео: 3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 3
- Видео: 3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 1
- Видео: 3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 2
- Видео: 3.9 Latent Dirichlet Allocation (LDA): Part 1
- Видео: 3.10 Latent Dirichlet Allocation (LDA): Part 2
- Practice Quiz: Week 3 Practice Quiz
- Reading: Programming Assignments Overview
Оцениваемый: Quiz: Week 3 Quiz
Оцениваемый: Programming Assignment
НЕДЕЛЯ 4
Week 4
During this module, you will learn text clustering, including the basic concepts, main clustering techniques, including probabilistic approaches and similarity-based approaches, and how to evaluate text clustering. You will also start learning text categorization, which is related to text clustering, but with pre-defined categories that can be viewed as pre-defining clusters.
9 видео, 1 материал для самостоятельного изучения, 1 тренировочный тест
- Reading: Week 4 Overview
- Видео: 4.2 Text Clustering: Generative Probabilistic Models Part 1
- Видео: 4.3 Text Clustering: Generative Probabilistic Models Part 2
- Видео: 4.4 Text Clustering: Generative Probabilistic Models Part 3
- Видео: 4.5 Text Clustering: Similarity-based Approaches
- Видео: 4.6 Text Clustering: Evaluation
- Видео: 4.7 Text Categorization: Motivation
- Видео: 4.8 Text Categorization: Methods
- Видео: 4.9 Text Categorization: Generative Probabilistic Models
- Practice Quiz: Week 4 Practice Quiz
Оцениваемый: Week 4 Quiz
НЕДЕЛЯ 5
Week 5
During this module, you will continue learning about various methods for text categorization, including multiple methods classified under discriminative classifiers, and you will also learn sentiment analysis and opinion mining, including a detailed introduction to a particular technique for sentiment classification (i.e., ordinal regression).
7 видео, 1 материал для самостоятельного изучения, 1 тренировочный тест
- Reading: Week 5 Overview
- Видео: 5.2 Text Categorization: Discriminative Classifier Part 2
- Видео: 5.3 Text Categorization: Evaluation Part 1
- Видео: 5.4 Text Categorization: Evaluation Part 2
- Видео: 5.5 Opinion Mining and Sentiment Analysis: Motivation
- Видео: 5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification
- Видео: 5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression
- Practice Quiz: Week 5 Practice Quiz
Оцениваемый: Week 5 Quiz
НЕДЕЛЯ 6
Week 6
During this module, you will continue learning about sentiment analysis and opinion mining with a focus on Latent Aspect Rating Analysis (LARA), and you will learn about techniques for joint mining of text and non-text data, including contextual text mining techniques for analyzing topics in text in association with various context information such as time, location, authors, and sources of data. You will also see a summary of the entire course.
8 видео, 1 материал для самостоятельного изучения, 1 тренировочный тест
- Reading: Week 6 Overview
- Видео: 6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 2
- Видео: 6.3 Text-Based Prediction
- Видео: 6.4 Contextual Text Mining: Motivation
- Видео: 6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis
- Видео: 6.6 Contextual Text Mining: Mining Topics with Social Network Context
- Видео: 6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision
- Видео: 6.8 Course Summary
- Practice Quiz: Week 6 Practice Quiz
Оцениваемый: Week 6 Quiz
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Авторы
Иллинойсский университет в Урбане-Шампейне
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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