Text Mining and Analytics

Text Mining and Analytics

This course is part of Data Mining Specialization

Instructor: ChengXiang Zhai

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

14 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

Build your subject-matter expertise

This course is part of the Data Mining Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

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.

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 14 minutes

Introduction to Text Mining and Analytics8 minutesPreview module
Course Prerequisites & Completion6 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

Orientation Quiz15 minutes
Pre-Quiz30 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 108 minutes

1.1 Overview Text Mining and Analytics: Part 111 minutesPreview module
1.2 Overview Text Mining and Analytics: Part 211 minutes
1.3 Natural Language Content Analysis: Part 112 minutes
1.4 Natural Language Content Analysis: Part 24 minutes
1.5 Text Representation: Part 110 minutes
1.6 Text Representation: Part 29 minutes
1.7 Word Association Mining and Analysis15 minutes
1.8 Paradigmatic Relation Discovery Part 114 minutes
1.9 Paradigmatic Relation Discovery Part 217 minutes

1 readingTotal 10 minutes

Week 1 Overview10 minutes

2 assignmentsTotal 120 minutes

Week 1 Quiz60 minutes
Week 1 Practice 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 115 minutes

2.1 Syntagmatic Relation Discovery: Entropy11 minutesPreview module
2.2 Syntagmatic Relation Discovery: Conditional Entropy11 minutes
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 113 minutes
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 29 minutes
2.5 Topic Mining and Analysis: Motivation and Task Definition7 minutes
2.6 Topic Mining and Analysis: Term as Topic11 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 Quiz60 minutes
Week 2 Practice 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 102 minutes

3.1 Probabilistic Topic Models: Mixture of Unigram Language Models12 minutesPreview module
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 210 minutes
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36 minutes
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110 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

Quiz: Week 3 Quiz60 minutes
Week 3 Practice 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: Motivation15 minutesPreview module
4.2 Text Clustering: Generative Probabilistic Models Part 116 minutes
4.3 Text Clustering: Generative Probabilistic Models Part 28 minutes
4.4 Text Clustering: Generative Probabilistic Models Part 314 minutes
4.5 Text Clustering: Similarity-based Approaches17 minutes
4.6 Text Clustering: Evaluation10 minutes
4.7 Text Categorization: Motivation14 minutes
4.8 Text Categorization: Methods11 minutes
4.9 Text Categorization: Generative Probabilistic Models31 minutes

1 readingTotal 10 minutes

Week 4 Overview10 minutes

2 assignmentsTotal 120 minutes

Week 4 Quiz60 minutes
Week 4 Practice 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 120 minutes

5.1 Text Categorization: Discriminative Classifier Part 120 minutesPreview module
5.2 Text Categorization: Discriminative Classifier Part 231 minutes
5.3 Text Categorization: Evaluation Part 114 minutes
5.4 Text Categorization: Evaluation Part 210 minutes
5.5 Opinion Mining and Sentiment Analysis: Motivation17 minutes
5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification11 minutes
5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression13 minutes

1 readingTotal 10 minutes

Week 5 Overview10 minutes

2 assignmentsTotal 120 minutes

Week 5 Quiz60 minutes
Week 5 Practice 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 119 minutes

6.1 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 115 minutesPreview module
6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 214 minutes
6.3 Text-Based Prediction12 minutes
6.4 Contextual Text Mining: Motivation6 minutes
6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis17 minutes
6.6 Contextual Text Mining: Mining Topics with Social Network Context14 minutes
6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision19 minutes
6.8 Course Summary18 minutes