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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
1. Chapter 1 - Introduction 2. What Is Content Analysis? 3. Why Use Computerized Analysis Techniques? 4. Standalone Tools Or Integrated Suites 5. Transitioning From Theory To Practice 6. Chapter 2 - Obtaining And Preparing Data 7. 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 8. Multimedia Content * Converting To Textual Format * Prosody 9. Example Data Sources * Patterns In Historical War Coverage * Competitive Intelligence * Global News Coverage 10. Downloading Content * Digital Content * Print Content 11. Preparing Content * Document Extraction * Cleaning * Post Filtering * Reforming/Reshaping * Content Proxy Extraction 12. Chapter 3 - Vocabulary Analysis 13. The Basics * Word Histograms * Readability Indexes * Normative Comparison * Non-Word Analysis * Colloquialisms: Abbreviations And Slang * Restricting The Analytical Window 14. Vocabulary Comparison And Evolution / Chronemics 15. Advanced Topics * Syllables, Rhyming, And 'Sounds Like' * Gender And Language * Authorship Attribution * Word Morphology, Stemming, And Lemmatization 16. 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 17. Chapter 5 - Lexicons, Entity Extraction, And Geocoding 18. Lexicons * Lexicons And Categorization * Lexical Correlation * Lexicon Consistency Checks * Thesauri And Vocabulary Expanders 19. Named Entity Extraction * Lexicons And Processing * Applications 20. Geocoding, Gazetteers, And Spatial Analysis * Geocoding * Gazetteers And The Geocoding Process * Operating Under Uncertainty * Spatial Analysis 21. Chapter 6 - Topic Extraction 22. How Machines Process Text * Unstructured Text * Extracting Meaning From Text 23. Applications Of Topic Extraction * Comparing/Clustering Documents * Automatic Summarization * Automatic Keyword Generation 24. Multilingual Analysis: Topic Extraction With Multiple Languages 25. Chapter 7 - Sentiment Analysis 26. Examining Emotions * Evolution * Evaluation * Analytical Resolution: Documents vs Objects * Hand-Crafted vs Automatically-Generated Lexicons * Other Sentiment Scales * Limitations * Measuring Language Rather Than Worldview 27. Chapter 8 - Similarity, Categorization and Clustering 28. 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 29. Clustering * Automated Clustering * Hierarchical Clustering * Partitional Clustering 30. Document Similarity * Vector Space Model * Contingency Tables 31. 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
