This research reference introduces readers to the data mining technologies available for use in content analysis research. Supporting the increasingly popular trend of employing digital analysis methodologies in the humanities, arts, and social sciences, this work provides crucial answers for researchers who are not familiar with data mining approaches and who do not know what they can do, how they work, or how their strengths and weaknesses match up to the strengths and weaknesses of human coded content analysis data. Offering valuable insights and guidance for using automated analytical…mehr
This research reference introduces readers to the data mining technologies available for use in content analysis research. Supporting the increasingly popular trend of employing digital analysis methodologies in the humanities, arts, and social sciences, this work provides crucial answers for researchers who are not familiar with data mining approaches and who do not know what they can do, how they work, or how their strengths and weaknesses match up to the strengths and weaknesses of human coded content analysis data. Offering valuable insights and guidance for using automated analytical techniques in content analysis research, this guide will appeal to both novice and experienced researchers throughout the humanities, arts, and social sciences.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
