Data Mining Methods for the Content Analyst

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.

• 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