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

DATA MINING METHODS FOR THE CONTENT ANALYST An Introduction to the Computational Analysis of Content
Copyright
Contents
List of Tables and Figures
Acknowledgments
1 Introduction
	What Is Content Analysis?
	Why Use Computerized Analysis Techniques?
	Standalone Tools or Integrated Suites
	Transitioning from Theory to Practice
	Chapter in Summary
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 in Summary
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 in Summary
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 in Summary
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 in Summary
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 in Summary
7 Sentiment Analysis
	Examining Emotions
		Evolution
		Evaluation
		Analytical Resolution: Documents versus Objects
		Hand-crafted versus Automatically Generated Lexicons
		Other Sentiment Scales
		Limitations
		Measuring Language Rather Than Worldview
	Chapter in Summary
8 Similarity, Categorization and Clustering
	Categorization
		The Vector Space Model
		Feature Selection
		Feature Reduction
		Learning Algorithm
		Evaluating ATC Results
		Benefi ts 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 in Summary
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
	Chapter in Summary
References
Index