Text Mining and Analytics
| Learning Experience | 9.0 |
|---|---|
| Content Rating | 9.2 |
Text mining cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making
About this Course
This course will cover the significant methods for mining and examining text data to find interesting patterns, extract helpful understanding, and support decision making, with a focus on analytical methods that can be typically used to arbitrary text data in any natural language without any or minimum human effort.
Detailed analysis of text data needs an understanding of natural language text, which is understood to be an uphill struggle for computer systems. However, a variety of analytical methods have actually been revealed to work well for the “shallow” however robust analysis of text information for pattern finding and understanding discovery. You will find out the standard principles, concepts, and significant algorithms in text mining and their possible applications.
Syllabus
The course on text mining and analytics has been offered by the University of Illinois at Urbana-Champaign.
Orientation on text mining and analytics
You will end up being acquainted with the course, your classmates, and our learning environment. The orientation will likewise assist you acquire the technical abilities needed for the course.
Week 1
During this module, you will find out the overall course style, an introduction of natural language processing methods and text representation, which are the foundation for all type of text-mining applications, and word association mining with a specific focus on mining among the 2 standard types of word associations (i.e., paradigmatic relations).
Week 2
During this module, you will discover more about word association mining with a specific focus on mining the other standard kind of word association (i.e., syntagmatic relations), and begin finding out subject analysis with a focus on methods for mining one subject from text.
Week 3
During this module, you will find out subject analysis in depth, consisting of mix designs and how they work, Expectation-Maximization (EM) algorithm and how it can be utilized to approximate specifications of a mix design, the standard subject design, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.
Week 4
During this module, you will find out text clustering, consisting of the standard principles, primary clustering methods, consisting of probabilistic methods and similarity-based methods, and how to assess text clustering. You will likewise begin finding out text classification, which relates to text clustering, however with pre-defined classifications that can be deemed pre-defining clusters.
Week 5
During this module, you will continue finding out about various techniques for text classification, including numerous techniques categorized under discriminative classifiers, and you will likewise find out sentiment analysis and opinion mining, consisting of a comprehensive intro to a specific method for sentiment classification (i.e., ordinal regression).
Week 6
During this module, you will continue finding out about sentiment analysis and opinion mining with a focus on Latent Aspect Rating Analysis (LARA), and you will discover methods for joint mining of text and non-text information, consisting of contextual text mining methods for examining subjects in text in association with numerous context details such as time, area, authors, and sources of data. You will likewise see a summary of the whole course.
Similar courses on Data Mining
Description
About this Course
This course will cover the significant methods for mining and examining text data to find interesting patterns, extract helpful understanding, and support decision making, with a focus on analytical methods that can be typically used to arbitrary text data in any natural language without any or minimum human effort.
Detailed analysis of text data needs an understanding of natural language text, which is understood to be an uphill struggle for computer systems. However, a variety of analytical methods have actually been revealed to work well for the “shallow” however robust analysis of text information for pattern finding and understanding discovery. You will find out the standard principles, concepts, and significant algorithms in text mining and their possible applications.
Syllabus
The course on text mining and analytics has been offered by the University of Illinois at Urbana-Champaign.
Orientation on text mining and analytics
You will end up being acquainted with the course, your classmates, and our learning environment. The orientation will likewise assist you acquire the technical abilities needed for the course.
Week 1
During this module, you will find out the overall course style, an introduction of natural language processing methods and text representation, which are the foundation for all type of text-mining applications, and word association mining with a specific focus on mining among the 2 standard types of word associations (i.e., paradigmatic relations).
Week 2
During this module, you will discover more about word association mining with a specific focus on mining the other standard kind of word association (i.e., syntagmatic relations), and begin finding out subject analysis with a focus on methods for mining one subject from text.
Week 3
During this module, you will find out subject analysis in depth, consisting of mix designs and how they work, Expectation-Maximization (EM) algorithm and how it can be utilized to approximate specifications of a mix design, the standard subject design, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.
Week 4
During this module, you will find out text clustering, consisting of the standard principles, primary clustering methods, consisting of probabilistic methods and similarity-based methods, and how to assess text clustering. You will likewise begin finding out text classification, which relates to text clustering, however with pre-defined classifications that can be deemed pre-defining clusters.
Week 5
During this module, you will continue finding out about various techniques for text classification, including numerous techniques categorized under discriminative classifiers, and you will likewise find out sentiment analysis and opinion mining, consisting of a comprehensive intro to a specific method for sentiment classification (i.e., ordinal regression).
Week 6
During this module, you will continue finding out about sentiment analysis and opinion mining with a focus on Latent Aspect Rating Analysis (LARA), and you will discover methods for joint mining of text and non-text information, consisting of contextual text mining methods for examining subjects in text in association with numerous context details such as time, area, authors, and sources of data. You will likewise see a summary of the whole course.
Similar courses on Data Mining
Specification:
- Coursera
- University of Illinois
- Online Course
- Self-paced
- Beginner
- 1-4 Weeks
- Free Trial (Paid Course & Certificate)
- English
- None Pre-requisite
- Data Mining Natural language processing Probability
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