About this course: 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.
Created by: University of Illinois at Urbana-Champaign
Taught by: ChengXiang Zhai, Professor
Department of Computer Science
| Basic Info | Course 3 of 6 in the Data Mining Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 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 videos, 5 readings, 1 practice quiz
- Reading: Welcome to Text Mining and Analytics!
- Reading: Syllabus
- Reading: About the Discussion Forums
- Reading: Updating your Profile
- Reading: Social Media
- Video: Introduction to Text Mining and Analytics
- Video: Course Prerequisites & Completion
- Practice Quiz: Pre-Quiz
Graded: 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 videos, 1 reading, 1 practice quiz
- Reading: Week 1 Overview
- Video: 1.1 Overview Text Mining and Analytics: Part 1
- Video: 1.2 Overview Text Mining and Analytics: Part 2
- Video: 1.3 Natural Language Content Analysis: Part 1
- Video: 1.4 Natural Language Content Analysis: Part 2
- Video: 1.5 Text Representation: Part 1
- Video: 1.6 Text Representation: Part 2
- Video: 1.7 Word Association Mining and Analysis
- Video: 1.8 Paradigmatic Relation Discovery Part 1
- Video: 1.9 Paradigmatic Relation Discovery Part 2
- Practice Quiz: Week 1 Practice Quiz
Graded: Week 1 Quiz
WEEK 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 videos, 1 reading, 1 practice quiz
- Reading: Week 2 Overview
- Video: 2.1 Syntagmatic Relation Discovery: Entropy
- Video: 2.2 Syntagmatic Relation Discovery: Conditional Entropy
- Video: 2.3 Syntagmatic Relation Discovery: Mutual Information: Part 1
- Video: 2.4 Syntagmatic Relation Discovery: Mutual Information: Part 2
- Video: 2.5 Topic Mining and Analysis: Motivation and Task Definition
- Video: 2.6 Topic Mining and Analysis: Term as Topic
- Video: 2.7 Topic Mining and Analysis: Probabilistic Topic Models
- Video: 2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 1
- Video: 2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 2
- Video: 2.10 Probabilistic Topic Models: Mining One Topic
- Practice Quiz: Week 2 Practice Quiz
Graded: Week 2 Quiz
WEEK 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 videos, 2 readings, 1 practice quiz
- Reading: Week 3 Overview
- Video: 3.1 Probabilistic Topic Models: Mixture of Unigram Language Models
- Video: 3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 1
- Video: 3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 2
- Video: 3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 1
- Video: 3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 2
- Video: 3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 3
- Video: 3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 1
- Video: 3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 2
- Video: 3.9 Latent Dirichlet Allocation (LDA): Part 1
- Video: 3.10 Latent Dirichlet Allocation (LDA): Part 2
- Practice Quiz: Week 3 Practice Quiz
- Reading: Programming Assignments Overview
Graded: Quiz: Week 3 Quiz
Graded: Programming Assignment
WEEK 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 videos, 1 reading, 1 practice quiz
- Reading: Week 4 Overview
- Video: 4.1 Text Clustering: Motivation
- Video: 4.2 Text Clustering: Generative Probabilistic Models Part 1
- Video: 4.3 Text Clustering: Generative Probabilistic Models Part 2
- Video: 4.4 Text Clustering: Generative Probabilistic Models Part 3
- Video: 4.5 Text Clustering: Similarity-based Approaches
- Video: 4.6 Text Clustering: Evaluation
- Video: 4.7 Text Categorization: Motivation
- Video: 4.8 Text Categorization: Methods
- Video: 4.9 Text Categorization: Generative Probabilistic Models
- Practice Quiz: Week 4 Practice Quiz
Graded: Week 4 Quiz
WEEK 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 videos, 1 reading, 1 practice quiz
- Reading: Week 5 Overview
- Video: 5.1 Text Categorization: Discriminative Classifier Part 1
- Video: 5.2 Text Categorization: Discriminative Classifier Part 2
- Video: 5.3 Text Categorization: Evaluation Part 1
- Video: 5.4 Text Categorization: Evaluation Part 2
- Video: 5.5 Opinion Mining and Sentiment Analysis: Motivation
- Video: 5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification
- Video: 5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression
- Practice Quiz: Week 5 Practice Quiz
Graded: Week 5 Quiz
WEEK 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 videos, 1 reading, 1 practice quiz
- Reading: Week 6 Overview
- Video: 6.1 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 1
- Video: 6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 2
- Video: 6.3 Text-Based Prediction
- Video: 6.4 Contextual Text Mining: Motivation
- Video: 6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis
- Video: 6.6 Contextual Text Mining: Mining Topics with Social Network Context
- Video: 6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision
- Video: 6.8 Course Summary
- Practice Quiz: Week 6 Practice Quiz
Graded: Week 6 Quiz
FAQs
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University of Illinois at Urbana-Champaign
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.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Most of the lessons are mathematical formulae in which, in my opinion, I need more real case study/practice to make myself clearly understand on how do those formulae perform.
Very complicated but useful for a deeper understanding of text mining
useful to make a big picture for text-mining and to learn several practical approaches.
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D
Hope the speaker can slow down sometimes.
It will be more helpful if give more real-world examples