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Text Mining and Analytics
  • Level Foundation
  • Duration 33 hours
  • Course by University of Illinois Urbana-Champaign
  • Offered by

About

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.

Modules

About the Course

    • 5 Readings
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5 Readings

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

Orientation Activities

    • 2 Videos
    • 1 Plugin
    • 2 Assignment
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2 Assignment

  • Orientation Quiz
  • Pre-Quiz

2 Videos

  • Introduction to Text Mining and Analytics
  • Course Prerequisites & Completion

Week 1 Information

    • 1 Readings
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1 Readings

  • Week 1 Overview

Week 1 Lessons

    • 9 Videos
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9 Videos

  • 1.1 Overview Text Mining and Analytics: Part 1
  • 1.2 Overview Text Mining and Analytics: Part 2
  • 1.3 Natural Language Content Analysis: Part 1
  • 1.4 Natural Language Content Analysis: Part 2
  • 1.5 Text Representation: Part 1
  • 1.6 Text Representation: Part 2
  • 1.7 Word Association Mining and Analysis
  • 1.8 Paradigmatic Relation Discovery Part 1
  • 1.9 Paradigmatic Relation Discovery Part 2

Week 1 Activities

    • 2 Assignment
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2 Assignment

  • Week 1 Practice Quiz
  • Week 1 Quiz

Week 2 Information

    • 1 Readings
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1 Readings

  • Week 2 Overview

Week 2 Lessons

    • 10 Videos
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10 Videos

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

Week 2 Activities

    • 2 Assignment
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2 Assignment

  • Week 2 Practice Quiz
  • Week 2 Quiz

Week 3 Information

    • 1 Readings
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1 Readings

  • Week 3 Overview

Week 3 Lessons

    • 10 Videos
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10 Videos

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

Week 3 Activities

    • 2 Assignment
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2 Assignment

  • Week 3 Practice Quiz
  • Quiz: Week 3 Quiz

Honors Track Programming Assignment

    • 1 Readings
    • 1 Programming
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1 Programming

  • Programming Assignment

1 Readings

  • Programming Assignments Overview

Week 4 Information

    • 1 Readings
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1 Readings

  • Week 4 Overview

Week 4 Lessons

    • 9 Videos
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9 Videos

  • 4.1 Text Clustering: Motivation
  • 4.2 Text Clustering: Generative Probabilistic Models Part 1
  • 4.3 Text Clustering: Generative Probabilistic Models Part 2
  • 4.4 Text Clustering: Generative Probabilistic Models Part 3
  • 4.5 Text Clustering: Similarity-based Approaches
  • 4.6 Text Clustering: Evaluation
  • 4.7 Text Categorization: Motivation
  • 4.8 Text Categorization: Methods
  • 4.9 Text Categorization: Generative Probabilistic Models

Week 4 Activities

    • 2 Assignment
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2 Assignment

  • Week 4 Practice Quiz
  • Week 4 Quiz

Week 5 Information

    • 1 Readings
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1 Readings

  • Week 5 Overview

Week 5 Lessons

    • 7 Videos
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7 Videos

  • 5.1 Text Categorization: Discriminative Classifier Part 1
  • 5.2 Text Categorization: Discriminative Classifier Part 2
  • 5.3 Text Categorization: Evaluation Part 1
  • 5.4 Text Categorization: Evaluation Part 2
  • 5.5 Opinion Mining and Sentiment Analysis: Motivation
  • 5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification
  • 5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression

Week 5 Activities

    • 2 Assignment
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2 Assignment

  • Week 5 Practice Quiz
  • Week 5 Quiz

Week 6 Information

    • 1 Readings
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1 Readings

  • Week 6 Overview

Week 6 Lessons

    • 8 Videos
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8 Videos

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

Week 6 Activities

    • 1 Plugin
    • 2 Assignment
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2 Assignment

  • Week 6 Practice Quiz
  • Week 6 Quiz

Auto Summary

Explore the fascinating world of text mining and analytics with this foundational course from Coursera. Designed for data science and AI enthusiasts, it delves into statistical techniques for analyzing text data to discover patterns and extract knowledge. With an emphasis on general applicability to any natural language, learners will master key concepts and algorithms essential for robust text analysis. The course spans 1980 minutes and offers flexible subscription options including Starter, Professional, and Paid plans, making it ideal for beginners eager to enhance their decision-making skills through text analytics.

ChengXiang Zhai
Instructor

ChengXiang Zhai