文本挖掘和分析

Text Mining and Analytics

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.

伊利诺伊大学香槟分校

Coursera

计算机

普通(中级)

20 小时

  • 英语
  • 1691

课程概况

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.

你将学到什么

Data Clustering Algorithms

Text Mining

Probabilistic Models

Sentiment Analysis

课程大纲

周1
完成时间为 2 小时
Orientation
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.
2 个视频 (总计 15 分钟), 5 个阅读材料, 2 个测验
完成时间为 4 小时
Week 1
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).
9 个视频 (总计 109 分钟), 1 个阅读材料, 2 个测验

周2
完成时间为 4 小时
Week 2
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.
10 个视频 (总计 116 分钟), 1 个阅读材料, 2 个测验

周3
完成时间为 10 小时
Week 3
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.
10 个视频 (总计 103 分钟), 2 个阅读材料, 3 个测验

周4
完成时间为 5 小时
Week 4
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.
9 个视频 (总计 141 分钟), 1 个阅读材料, 2 个测验

周5
完成时间为 4 小时
Week 5
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).
7 个视频 (总计 121 分钟), 1 个阅读材料, 2 个测验

周6
完成时间为 4 小时
Week 6
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.

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