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Text Mining and Analytics

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Text Mining and Analytics

This course is part of Data Mining Specialization

Instructor: ChengXiang Zhai

75,094 already enrolled

Included with

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7 modules

Gain insight into a topic and learn the fundamentals.

741 reviews

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

92%

Most learners liked this course

7 modules

Gain insight into a topic and learn the fundamentals.

741 reviews

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

92%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Classification Algorithms

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

14 assignments

Taught in English

91% of learners achieved a positive career outcome

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This course is part of the Data Mining Specialization

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There are 7 modules in 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.

Module details

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.

What's included

2 videos5 readings2 assignments1 plugin

2 videosTotal 15 minutes

Introduction to Text Mining and Analytics8 minutes
Course Prerequisites & Completion7 minutes

5 readingsTotal 60 minutes

Welcome to Text Mining and Analytics!10 minutes
Syllabus15 minutes
About the Discussion Forums15 minutes
Updating your Profile10 minutes
Social Media10 minutes

2 assignmentsTotal 45 minutes

Pre-Quiz30 minutes
Orientation Quiz15 minutes

1 pluginTotal 15 minutes

Welcome! Please tell us about yourself.15 minutes

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).

What's included

9 videos1 reading2 assignments

9 videosTotal 109 minutes

1.1 Overview Text Mining and Analytics: Part 112 minutes
1.2 Overview Text Mining and Analytics: Part 212 minutes
1.3 Natural Language Content Analysis: Part 113 minutes
1.4 Natural Language Content Analysis: Part 24 minutes
1.5 Text Representation: Part 111 minutes
1.6 Text Representation: Part 29 minutes
1.7 Word Association Mining and Analysis16 minutes
1.8 Paradigmatic Relation Discovery Part 115 minutes
1.9 Paradigmatic Relation Discovery Part 218 minutes

1 readingTotal 10 minutes

Week 1 Overview10 minutes

2 assignmentsTotal 120 minutes

Week 1 Practice Quiz60 minutes
Week 1 Quiz60 minutes

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.

What's included

10 videos1 reading2 assignments

10 videosTotal 116 minutes

2.1 Syntagmatic Relation Discovery: Entropy11 minutes
2.2 Syntagmatic Relation Discovery: Conditional Entropy12 minutes
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 114 minutes
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 210 minutes
2.5 Topic Mining and Analysis: Motivation and Task Definition8 minutes
2.6 Topic Mining and Analysis: Term as Topic12 minutes
2.7 Topic Mining and Analysis: Probabilistic Topic Models14 minutes
2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 110 minutes
2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 213 minutes
2.10 Probabilistic Topic Models: Mining One Topic12 minutes

1 readingTotal 10 minutes

Week 2 Overview10 minutes

2 assignmentsTotal 120 minutes

Week 2 Practice Quiz60 minutes
Week 2 Quiz60 minutes

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.

What's included

10 videos2 readings2 assignments1 programming assignment

10 videosTotal 103 minutes

3.1 Probabilistic Topic Models: Mixture of Unigram Language Models13 minutes
3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110 minutes
3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28 minutes
3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111 minutes
3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 211 minutes
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36 minutes
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 111 minutes
3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210 minutes
3.9 Latent Dirichlet Allocation (LDA): Part 110 minutes
3.10 Latent Dirichlet Allocation (LDA): Part 212 minutes

2 readingsTotal 20 minutes

Week 3 Overview10 minutes
Programming Assignments Overview10 minutes

2 assignmentsTotal 120 minutes

Week 3 Practice Quiz60 minutes
Quiz: Week 3 Quiz60 minutes

1 programming assignmentTotal 360 minutes

Programming Assignment360 minutes

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.

What's included

9 videos1 reading2 assignments

9 videosTotal 141 minutes

4.1 Text Clustering: Motivation16 minutes
4.2 Text Clustering: Generative Probabilistic Models Part 116 minutes
4.3 Text Clustering: Generative Probabilistic Models Part 29 minutes
4.4 Text Clustering: Generative Probabilistic Models Part 315 minutes
4.5 Text Clustering: Similarity-based Approaches18 minutes
4.6 Text Clustering: Evaluation10 minutes
4.7 Text Categorization: Motivation15 minutes
4.8 Text Categorization: Methods12 minutes
4.9 Text Categorization: Generative Probabilistic Models31 minutes

1 readingTotal 10 minutes

Week 4 Overview10 minutes

2 assignmentsTotal 120 minutes

Week 4 Practice Quiz60 minutes
Week 4 Quiz60 minutes

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).

What's included

7 videos1 reading2 assignments

7 videosTotal 121 minutes

5.1 Text Categorization: Discriminative Classifier Part 121 minutes
5.2 Text Categorization: Discriminative Classifier Part 232 minutes
5.3 Text Categorization: Evaluation Part 114 minutes
5.4 Text Categorization: Evaluation Part 211 minutes
5.5 Opinion Mining and Sentiment Analysis: Motivation18 minutes
5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification12 minutes
5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression14 minutes

1 readingTotal 10 minutes

Week 5 Overview10 minutes

2 assignmentsTotal 120 minutes

Week 5 Practice Quiz60 minutes
Week 5 Quiz60 minutes

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.

What's included

8 videos1 reading2 assignments1 plugin

8 videosTotal 120 minutes

6.1 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 115 minutes
6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 215 minutes
6.3 Text-Based Prediction12 minutes
6.4 Contextual Text Mining: Motivation7 minutes
6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis18 minutes
6.6 Contextual Text Mining: Mining Topics with Social Network Context15 minutes
6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision20 minutes
6.8 Course Summary19 minutes

1 readingTotal 10 minutes

Week 6 Overview10 minutes

2 assignmentsTotal 120 minutes

Week 6 Practice Quiz60 minutes
Week 6 Quiz60 minutes

1 pluginTotal 15 minutes

How was the course?15 minutes

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Instructor

Instructor ratings

(55 ratings)

ChengXiang Zhai

University of Illinois Urbana-Champaign

4 Courses110,196 learners

Offered by

University of Illinois Urbana-Champaign

Learner reviews

5 stars
68.69%
4 stars
20.10%
3 stars
7.55%
2 stars
2.02%
1 star
1.61%

Showing 3 of 741

Reviewed on Mar 24, 2018

The content of Text Mining and Analytics is very comprehensive and deep. More practise about how formula works would be better. Quiz could be not tough to be completed after attending every lectures.

Reviewed on Jul 26, 2020

Well taught course, would have preferred if went further into topics like opinion mining (even if we had optional lectures or assignments). However, definitely a fun course to learn a lot.

Reviewed on Jul 22, 2017

The workflow is clear and the professor speaks to the students directly about all aspects without skimming the material.

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