Data Mining Methods for the Content Analyst
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Data Mining Methods for the Content Analyst : An Introduction to the Computational Analysis of Content

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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.
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Product details

  • Paperback | 106 pages
  • 152 x 226 x 10mm | 249.47g
  • ROUTLEDGE
  • London, United Kingdom
  • English
  • 6 Tables, black and white
  • 0415895146
  • 9780415895149
  • 1,283,220

Table of contents

Chapter 1 - Introduction










What Is Content Analysis?







Why Use Computerized Analysis Techniques?







Standalone Tools Or Integrated Suites







Transitioning From Theory To Practice









Chapter 2 - Obtaining And Preparing Data









Collecting Data From Digital Text Repositories









Are The Data Meaningful?







Using Data In Unintended Ways







Analytical Resolution







Types Of Data Sources







Finding Sources







Searching Text Collections







Sources Of Incompleteness







Licensing Restrictions And Content Blackouts







Measuring Viewership







Accuracy And Convenience Samples







Random Samples









Multimedia Content









Converting To Textual Format







Prosody









Example Data Sources









Patterns In Historical War Coverage







Competitive Intelligence







Global News Coverage









Downloading Content









Digital Content







Print Content









Preparing Content









Document Extraction







Cleaning







Post Filtering







Reforming/Reshaping







Content Proxy Extraction











Chapter 3 - Vocabulary Analysis









The Basics









Word Histograms







Readability Indexes







Normative Comparison







Non-Word Analysis







Colloquialisms: Abbreviations And Slang







Restricting The Analytical Window









Vocabulary Comparison And Evolution / Chronemics







Advanced Topics









Syllables, Rhyming, And `Sounds Like'







Gender And Language







Authorship Attribution







Word Morphology, Stemming, And Lemmatization











Chapter 4 - Correlation And Co-Occurrence









Understanding Correlation







Computing Word Correlations







Directionality







Concordance







Co-Occurrence And Search







Language Variation And Lexicons







Non-Co-Occurrence







Correlation With Metadata









Chapter 5 - Lexicons, Entity Extraction, And Geocoding









Lexicons









Lexicons And Categorization







Lexical Correlation







Lexicon Consistency Checks







Thesauri And Vocabulary Expanders









Named Entity Extraction









Lexicons And Processing







Applications









Geocoding, Gazetteers, And Spatial Analysis









Geocoding







Gazetteers And The Geocoding Process







Operating Under Uncertainty







Spatial Analysis











Chapter 6 - Topic Extraction









How Machines Process Text









Unstructured Text







Extracting Meaning From Text









Applications Of Topic Extraction









Comparing/Clustering Documents







Automatic Summarization







Automatic Keyword Generation









Multilingual Analysis: Topic Extraction With Multiple Languages









Chapter 7 - Sentiment Analysis









Examining Emotions









Evolution







Evaluation







Analytical Resolution: Documents vs Objects







Hand-Crafted vs Automatically-Generated Lexicons







Other Sentiment Scales







Limitations







Measuring Language Rather Than Worldview











Chapter 8 - Similarity, Categorization and Clustering









Categorization









The Vector-Space Model







Feature Selection







Feature Reduction







Learning Algorithm







Evaluating ATC Results







Benefits of ATC Over Human Categorization







Limitations of ATC







Applications of ATC









Clustering









Automated Clustering







Hierarchical Clustering







Partitional Clustering









Document Similarity









Vector Space Model







Contingency Tables











Chapter 9 - Network Analysis









Understanding Network Analysis







Network Content Analysis







Representing Network Data







Constructing the Network







Network Structure







The Triad Census







Network Evolution







Visualization and Clustering
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About Kalev Leetaru

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.
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