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.
课程信息
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学习Course的学生是
- Data Scientists
- Data Analysts
- Machine Learning Engineers
- Librarians
- Data Engineers
您将获得的技能
Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis
教学大纲 - 您将从这门课程中学到什么
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
5 个阅读材料
Welcome to Text Mining and Analytics!10分钟
Syllabus15分钟
About the Discussion Forums15分钟
Updating your Profile10分钟
Social Media10分钟
2 个练习
Orientation Quiz15分钟
Pre-Quiz26分钟
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
9 个视频
1.2 Overview Text Mining and Analytics: Part 211分钟
1.3 Natural Language Content Analysis: Part 112分钟
1.4 Natural Language Content Analysis: Part 24分钟
1.5 Text Representation: Part 110分钟
1.6 Text Representation: Part 29分钟
1.7 Word Association Mining and Analysis15分钟
1.8 Paradigmatic Relation Discovery Part 114分钟
1.9 Paradigmatic Relation Discovery Part 217分钟
1 个阅读材料
Week 1 Overview10分钟
2 个练习
Week 1 Practice Quiz1小时
Week 1 Quiz1小时
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
10 个视频
2.2 Syntagmatic Relation Discovery: Conditional Entropy11分钟
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 113分钟
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 29分钟
2.5 Topic Mining and Analysis: Motivation and Task Definition7分钟
2.6 Topic Mining and Analysis: Term as Topic11分钟
2.7 Topic Mining and Analysis: Probabilistic Topic Models14分钟
2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 110分钟
2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 213分钟
2.10 Probabilistic Topic Models: Mining One Topic12分钟
1 个阅读材料
Week 2 Overview10分钟
2 个练习
Week 2 Practice Quiz1小时
Week 2 Quiz1小时
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
10 个视频
3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110分钟
3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28分钟
3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111分钟
3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 210分钟
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36分钟
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110分钟
3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210分钟
3.9 Latent Dirichlet Allocation (LDA): Part 110分钟
3.10 Latent Dirichlet Allocation (LDA): Part 212分钟
2 个阅读材料
Week 3 Overview10分钟
Programming Assignments Overview10分钟
2 个练习
Week 3 Practice Quiz1小时
Quiz: Week 3 Quiz1小时
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
9 个视频
4.2 Text Clustering: Generative Probabilistic Models Part 116分钟
4.3 Text Clustering: Generative Probabilistic Models Part 28分钟
4.4 Text Clustering: Generative Probabilistic Models Part 314分钟
4.5 Text Clustering: Similarity-based Approaches17分钟
4.6 Text Clustering: Evaluation10分钟
4.7 Text Categorization: Motivation14分钟
4.8 Text Categorization: Methods11分钟
4.9 Text Categorization: Generative Probabilistic Models31分钟
1 个阅读材料
Week 4 Overview10分钟
2 个练习
Week 4 Practice Quiz1小时
Week 4 Quiz1小时
4.4
102 个审阅33%
完成这些课程后已开始新的职业生涯
42%
通过此课程获得实实在在的工作福利
12%
加薪或升职
来自文本挖掘和分析的热门评论
Excellent course, the pipeline they propose to help you understand text mining is quite helpful. It has an important introduction to the most key concepts and techniques for text mining and analytics.
The content of Text Mining and Analytics is very comprehensive and deep. More practise about how formula works would be better. Quiz could be not tough to be completed after attending every lectures.
讲师
ChengXiang Zhai
ProfessorDepartment of Computer Science
立即开始攻读硕士学位
此 课程 隶属于 伊利诺伊大学香槟分校 提供的 100% 在线 Master in Computer Science。如果您被录取参加全部课程，您的课程将计入您的学位学习进程。
关于 伊利诺伊大学香槟分校
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
关于 数据挖掘 专项课程
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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