Text Mining and Analytics
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Text Mining and Analytics
This course is part of Data Mining Specialization
Instructor: ChengXiang Zhai
75,184 already enrolled
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Skills you'll gain
- Probability & Statistics
- Statistical Methods
- Unsupervised Learning
- Data Analysis
- Data-Driven Decision-Making
- Data Mining
- Natural Language Processing
- Statistical Analysis
- Probability Distribution
- Text Mining
- Analytics
- Model Optimization
- Applied Machine Learning
- Statistical Machine Learning
- Correlation Analysis
- Generative Model Architectures
- Unstructured Data
Tools you'll learn
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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
1 reading•Total 10 minutes
- Week 6 Overview•10 minutes
2 assignments•Total 120 minutes
- Week 6 Practice Quiz•60 minutes
- Week 6 Quiz•60 minutes
1 plugin•Total 15 minutes
- How was the course?•15 minutes
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Reviewed on Apr 10, 2019
The course was very challenging and i learn a lot of new things from the course, this will help to complete my project.
Reviewed on Jul 22, 2017
The workflow is clear and the professor speaks to the students directly about all aspects without skimming the material.
Reviewed on Feb 24, 2017
Very good course thank you I wish we could have use case applications with high level tools such as RThanks a lot again !
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