Text Mining and Analytics

Text Mining and Analytics

This course is part of Data Mining Specialization

Instructor: ChengXiang Zhai

Access provided by American College of Technology (ACT)

74,457 already enrolled

7 modules

Gain insight into a topic and learn the fundamentals.

738 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.

738 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

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Assessments

14 assignments

Taught in English

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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.

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 videos Total 15 minutes

Introduction to Text Mining and Analytics 8 minutes
Course Prerequisites & Completion 7 minutes

5 readings Total 60 minutes

Welcome to Text Mining and Analytics! 10 minutes
Syllabus 15 minutes
About the Discussion Forums 15 minutes
Updating your Profile 10 minutes
Social Media 10 minutes

2 assignments Total 45 minutes

Pre-Quiz 30 minutes
Orientation Quiz 15 minutes

1 plugin Total 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 videos Total 109 minutes

1.1 Overview Text Mining and Analytics: Part 1 12 minutes
1.2 Overview Text Mining and Analytics: Part 2 12 minutes
1.3 Natural Language Content Analysis: Part 1 13 minutes
1.4 Natural Language Content Analysis: Part 2 4 minutes
1.5 Text Representation: Part 1 11 minutes
1.6 Text Representation: Part 2 9 minutes
1.7 Word Association Mining and Analysis 16 minutes
1.8 Paradigmatic Relation Discovery Part 1 15 minutes
1.9 Paradigmatic Relation Discovery Part 2 18 minutes

1 reading Total 10 minutes

Week 1 Overview 10 minutes

2 assignments Total 120 minutes

Week 1 Practice Quiz 60 minutes
Week 1 Quiz 60 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 videos Total 116 minutes

2.1 Syntagmatic Relation Discovery: Entropy 11 minutes
2.2 Syntagmatic Relation Discovery: Conditional Entropy 12 minutes
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 1 14 minutes
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 2 10 minutes
2.5 Topic Mining and Analysis: Motivation and Task Definition 8 minutes
2.6 Topic Mining and Analysis: Term as Topic 12 minutes
2.7 Topic Mining and Analysis: Probabilistic Topic Models 14 minutes
2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 1 10 minutes
2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 2 13 minutes
2.10 Probabilistic Topic Models: Mining One Topic 12 minutes

1 reading Total 10 minutes

Week 2 Overview 10 minutes

2 assignments Total 120 minutes

Week 2 Practice Quiz 60 minutes
Week 2 Quiz 60 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 videos Total 103 minutes

3.1 Probabilistic Topic Models: Mixture of Unigram Language Models 13 minutes
3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 1 10 minutes
3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 2 8 minutes
3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 1 11 minutes
3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 2 11 minutes
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 3 6 minutes
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 1 11 minutes
3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 2 10 minutes
3.9 Latent Dirichlet Allocation (LDA): Part 1 10 minutes
3.10 Latent Dirichlet Allocation (LDA): Part 2 12 minutes

2 readings Total 20 minutes

Week 3 Overview 10 minutes
Programming Assignments Overview 10 minutes

2 assignments Total 120 minutes

Week 3 Practice Quiz 60 minutes
Quiz: Week 3 Quiz 60 minutes

1 programming assignment Total 360 minutes

Programming Assignment 360 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 videos Total 141 minutes

4.1 Text Clustering: Motivation 16 minutes
4.2 Text Clustering: Generative Probabilistic Models Part 1 16 minutes
4.3 Text Clustering: Generative Probabilistic Models Part 2 9 minutes
4.4 Text Clustering: Generative Probabilistic Models Part 3 15 minutes
4.5 Text Clustering: Similarity-based Approaches 18 minutes
4.6 Text Clustering: Evaluation 10 minutes
4.7 Text Categorization: Motivation 15 minutes
4.8 Text Categorization: Methods 12 minutes
4.9 Text Categorization: Generative Probabilistic Models 31 minutes

1 reading Total 10 minutes

Week 4 Overview 10 minutes

2 assignments Total 120 minutes

Week 4 Practice Quiz 60 minutes
Week 4 Quiz 60 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 videos Total 121 minutes

5.1 Text Categorization: Discriminative Classifier Part 1 21 minutes
5.2 Text Categorization: Discriminative Classifier Part 2 32 minutes
5.3 Text Categorization: Evaluation Part 1 14 minutes
5.4 Text Categorization: Evaluation Part 2 11 minutes
5.5 Opinion Mining and Sentiment Analysis: Motivation 18 minutes
5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification 12 minutes
5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression 14 minutes

1 reading Total 10 minutes

Week 5 Overview 10 minutes

2 assignments Total 120 minutes

Week 5 Practice Quiz 60 minutes
Week 5 Quiz 60 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 videos Total 120 minutes

6.1 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 1 15 minutes
6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 2 15 minutes
6.3 Text-Based Prediction 12 minutes
6.4 Contextual Text Mining: Motivation 7 minutes
6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis 18 minutes
6.6 Contextual Text Mining: Mining Topics with Social Network Context 15 minutes
6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision 20 minutes
6.8 Course Summary 19 minutes