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With continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike.
Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.
- Sales Rank: #1276335 in Books
- Published on: 2011-12-15
- Released on: 2012-01-31
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .28" w x 6.00" l, .45 pounds
- Binding: Paperback
- 120 pages
About the Author
Kalev Leetaru is Senior Research Scientist for Content Analysis at the University of Illinois Institute for Computing in Humanities, Arts, and Social Science and Center Affiliate of the National Center for Supercomputing Applications. He leads a number of large initiatives centering on the application of high performance computing to grand challenge problems using massive-scale document and data archives.
Most helpful customer reviews
9 of 9 people found the following review helpful.
Far too basic to be of any practical use
By Trey
Leetaru produces interesting work with large datasets and has received substantial coverage for doing so. That said, it is surprising that this book is so light on pragmatic content (less than 100 pages of actual content). It may be useful for a reader with absolutely no background in quantitative or computer-assisted text analysis, but will provide no real how-to advice for the aspiring analyst. Each chapter provides a general overview of terms for various areas of text analysis, sometimes providing a link or two to potential starting points, but often speaking broadly about possible approaches. Users interested in collecting/scraping their own data will find no pointers here.
Surprisingly, Leetaru recommends using Stata (it's not all upper case, despite many people writing it as such), SPSS, or SAS for their "large user and developer communities, with users able to submit custom modules to be shared with other members, developing a community-based collection of specialty tools" (p. 5). In fact, R is far more suited for text analysis, is free, open source, and genuinely has a large community of developers.
Further, Leetaru recommends that users learn Perl (also should not be capitalized despite being an acronym). Perl is well-suited for text-manipulation but many beginning programmers may find its syntax opaque. They may be better off learning Python and starting with the free edition of Natural Language Processing with Python ([...]).
I had high hopes for this book as a social scientist who uses quantitative text analysis in my work. Although a primer on programming, scraping, text analysis, and related topics would be far too much for a single text, the book barely scratches the surface of any of these topics and readers will likely need to turn elsewhere for practical advice on any of them.
2 of 2 people found the following review helpful.
Bridging Disciplines
By John M. Ford
Kalev Hannes Leetaru recognizes what some researchers are slow to see: That similar problems are tackled in different ways by researchers in different disciplines. And that cross-fertilization between these disciplines strengthens both. This book introduces researchers familiar with social science content analysis to computational methods developed in text analytics and other "big data" disciplines.
The book begins with an extended discussion of digital content: where to find it, how to clean it up for analysis, and what research practices to follow to ensure proper sampling and representativeness. The next chapter introduces vocabulary analysis. It covers major techniques based on word and phrase frequencies and outlines their strengths and weaknesses. The following chapter describes what can be learned by summarizing which words in a text tend to appear near other words. Building on these basics, the author describes how software tools are used to build lexicons that describe a language comprehensively and make inferences about people, places, and organizations. He discusses how the superficial characteristics of a text are processed to build deeper semantic representations of the its topic and key concepts. A seperate chapter introduces sentiment analysis, and describes techniques for modeling the emotional tone of written text.
Information from preceding chapters is integrated into an introduction to more advanced statistical techniques such as similarity, categorization, and clustering. This chapter includes an accessible discussions of the role of vectors in describing relationships between texts and the challenges of reducing an abundance of information to a manageable set of key features. It closes with examples of practical applications of text analysis techniques and demonstrates how to evaluate their effectiveness. A final chapter presents the math and methods used to formally describe networks. Readers are shown how these techniques apply to both online communities such as Facebook and real world relationships between people. The discussion ends rather abruptly with this chapter--it could benefit from a closing summary and recommendations for further reading.
The book strikes an effective balance between the needs of beginning and more experienced readers. The language and level of explanation is introductory. But the software, references, and resources are central to text and content analysis. They help readers transition to more advanced knowledge of both disciplines. Resources mentioned include (Amazon won't let me include web sites, but they are there in the book.):
- The General Architecture for Text Engineering (GATE)
- The National Resource for Computational Content Analysis (NRCCA)
- The Fulltext Sources Online directory
- The WordNet database
- The Geographic Names Information System (GNIS)
Leetaru's book does its discipline bridging reasonably well. Readers who like this approach might also benefit from Matthew Jockers' Macroanalysis which was written to introduce text analysis technology to researchers in the digital humanities. It provides greater description of both methods and resources.
1 of 1 people found the following review helpful.
A concise and valuable introduction to data-mining methods for the non-specialist
By AutoMan
Leetaru's book aims to provide content analysts who have no background in data-mining techniques with a primer on the core concepts and technical capabilities of data-mining approaches. In doing so, it fills a gaping void in the methodological literature on content analysis that is currently available to scholars in the social sciences and humanities.
No other book (or review article, for that matter) has translated for the "rest of us" a vast amount of conceptual and analytical material generated by computer scientists for use in data mining methods. Most users of human-coded content analysis methods simply don't know what the data mining approaches are, what they can do, how they work, or how their strengths and weaknesses match up to the strengths and weaknesses of human coded content analysis data.
This book aims to fill that gap, and it does so admirably. Its concise treatment of these topics makes Data Mining Methods for the Content Analyst a valuable resource for introducing graduate students and seasoned researchers to the possibilities that large-scale data mining techniques have to offer. The clarity of its prose makes this book accessible to undergraduate students and interested laypersons without any background in quantitative analysis.
The methodology of data mining is moving so quickly that a book on the "latest techniques" would be quickly rendered obsolete. This book's more sensible and durable approach is to provide a conceptual overview of what data mining can do, and this overview will remain a valuable contribution for many years to come.
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