Data mining methods for the content analyst : an introduction to the computational analysis of content

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.

• List of Tables and Figures p. xi
• Acknowledgments p. xiii
• 1 Introduction p. 1
• What Is Content Analysis? p. 1
• Why Use Computerized Analysis Techniques? p. 2
• Standalone Tools or Integrated Suites p. 3
• Transitioning from Theory to Practice p. 5
• Chapter in Summary p. 6
• 2 Obtaining and Preparing Data p. 7
• Collecting Data from Digital Text Repositories p. 7
• Are the Data Meaningful? p. 8
• Using Data in Unintended Ways p. 9
• Analytical Resolution p. 10
• Types of Data Sources p. 11
• Finding Sources p. 12
• Searching Text Collections p. 13
• Sources of Incompleteness p. 14
• Licensing Restrictions and Content Blackouts p. 16
• Measuring Viewership p. 17
• Accuracy and Convenience Samples p. 17
• Random Samples p. 18
• Multimedia Content p. 19
• Converting to Textual Format p. 19
• Prosody p. 19
• Example Data Sources p. 20
• Patterns in Historical War Coverage p. 20
• Competitive Intelligence p. 20
• Global News Coverage p. 21
• Downloading Content p. 22
• Digital Content p. 22
• Print Content p. 23
• Preparing Content p. 23
• Document Extraction p. 23
• Cleaning p. 24
• Post Filtering p. 24
• Reforming/Reshaping p. 25
• Content Proxy Extraction p. 25
• Chapter in Summary p. 25
• 3 Vocabulary Analysis p. 26
• The Basics p. 26
• Word Histograms p. 26
• Readability Indexes p. 27
• Normative Comparison p. 28
• Non-word Analysis p. 28
• Colloquialisms: Abbreviations and Slang p. 29
• Restricting the Analytical Window p. 29
• Vocabulary Comparison and Evolution/Chronemics p. 30
• Advanced Topics p. 32
• Syllables, Rhyming, and "Sounds Like" p. 32
• Gender and Language p. 33
• Authorship Attribution p. 33
• Word Morphology, Stemming, and Lemmatization p. 33
• Chapter in Summary p. 34
• 4 Correlation and Co-occurrence p. 36
• Understanding Correlation p. 36
• Computing Word Correlations p. 37
• Directionality p. 38
• Concordance p. 39
• Co-occurrence and Search p. 40
• Language Variation and Lexicons p. 40
• Non-co-occurrence p. 41
• Correlation with Metadata p. 41
• Chapter in Summary p. 42
• 5 Lexicons, Entity Extraction, and Geocoding p. 43
• Lexicons p. 43
• Lexicons and Categorization p. 44
• Lexical Correlation p. 45
• Lexicon Consistency Checks p. 45
• Thesauri and Vocabulary Expanders p. 47
• Named Entity Extraction p. 48
• Lexicons and Processing p. 48
• Applications p. 49
• Geocoding, Gazetteers, and Spatial Analysis p. 51
• Geocoding p. 51
• Gazetteers and the Geocoding Process p. 52
• Operating Under Uncertainty p. 54
• Spatial Analysis p. 55
• Chapter in Summary p. 56
• 6 Topic Extraction p. 57
• How Machines Process Text p. 57
• Unstructured Text p. 58
• Extracting Meaning from Text p. 58
• Applications of Topic Extraction p. 59
• Comparing/Clustering Documents p. 60
• Automatic Summarization p. 60
• Automatic Keyword Generation p. 61
• Multilingual Analysis: Topic Extraction with Multiple Languages p. 62
• Chapter in Summary p. 63
• 7 Sentiment Analysis p. 65
• Examining Emotions p. 65
• Evolution p. 65
• Evaluation p. 66
• Analytical Resolution: Documents versus Objects p. 67
• Hand-crafted versus Automatically Generated Lexicons p. 68
• Other Sentiment Scales p. 68
• Limitations p. 69
• Measuring Language Rather Than Worldview p. 69
• Chapter in Summary p. 70
• 8 Similarity, Categorization and Clustering p. 71
• Categorization p. 71
• The Vector Space Model p. 72
• Feature Selection p. 72
• Feature Reduction p. 73
• Learning Algorithm p. 74
• Evaluating ATC Results p. 75
• Benefits of ATC over Human Categorization p. 77
• Limitations of ATC p. 78
• Applications of ATC p. 80
• Clustering p. 80
• Automated Clustering p. 81
• Hierarchical Clustering p. 82
• Partitional Clustering p. 82
• Document Similarity p. 83
• Vector Space Model p. 84
• Contingency Tables p. 84
• Chapter in Summary p. 85
• 9 Network Analysis p. 86
• Understanding Network Analysis p. 86
• Network Content Analysis p. 87
• Representing Network Data p. 88
• Constructing the Network p. 89
• Network Structure p. 89
• The Triad Census p. 91
• Network Evolution p. 91
• Visualization and Clustering p. 92
• Chapter in Summary p. 96
• References p. 97
• Index p. 100