book

Data Mining Methods for the Content Analyst

by Kalev Leetaru

November 2012

Beginner to intermediate

106 pages

3h 39m

English

Routledge

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What Is Content Analysis?Why Use Computerized Analysis Techniques?Standalone Tools or Integrated SuitesTransitioning from Theory to PracticeChapter in Summary
Collecting Data from Digital Text RepositoriesAre the Data Meaningful?Using Data in Unintended WaysAnalytical ResolutionTypes of Data SourcesFinding SourcesSearching Text CollectionsSources of IncompletenessLicensing Restrictions and Content BlackoutsMeasuring ViewershipAccuracy and Convenience SamplesRandom SamplesMultimedia ContentConverting to Textual FormatProsodyExample Data SourcesPatterns in Historical War CoverageCompetitive IntelligenceGlobal News CoverageDownloading ContentDigital ContentPrint ContentPreparing ContentDocument ExtractionCleaningPost FilteringReforming/ReshapingContent Proxy ExtractionChapter in Summary

The BasicsWord HistogramsReadability IndexesNormative ComparisonNon-word AnalysisColloquialisms: Abbreviations and SlangRestricting the Analytical WindowVocabulary Comparison and Evolution/ChronemicsAdvanced TopicsSyllables, Rhyming, and “Sounds Like”Gender and LanguageAuthorship AttributionWord Morphology, Stemming, and LemmatizationChapter in Summary
Understanding CorrelationComputing Word CorrelationsDirectionalityConcordanceCo-occurrence and SearchLanguage Variation and LexiconsNon-co-occurrenceCorrelation with MetadataChapter in Summary
LexiconsLexicons and CategorizationLexical CorrelationLexicon Consistency ChecksThesauri and Vocabulary ExpandersNamed Entity ExtractionLexicons and ProcessingApplicationsGeocoding, Gazetteers, and Spatial AnalysisGeocodingGazetteers and the Geocoding ProcessOperating Under UncertaintySpatial AnalysisChapter in Summary
How Machines Process TextUnstructured TextExtracting Meaning from TextApplications of Topic ExtractionComparing/Clustering DocumentsAutomatic SummarizationAutomatic Keyword GenerationMultilingual Analysis: Topic Extraction with Multiple LanguagesChapter in Summary
Examining EmotionsEvolutionEvaluationAnalytical Resolution: Documents versus ObjectsHand-crafted versus Automatically Generated LexiconsOther Sentiment ScalesLimitationsMeasuring Language Rather Than WorldviewChapter in Summary
CategorizationThe Vector Space ModelFeature SelectionFeature ReductionLearning AlgorithmEvaluating ATC ResultsBenefits of ATC over Human CategorizationLimitations of ATCApplications of ATCClusteringAutomated ClusteringHierarchical ClusteringPartitional ClusteringDocument SimilarityVector Space ModelContingency TablesChapter in Summary
Understanding Network AnalysisNetwork Content AnalysisRepresenting Network DataConstructing the NetworkNetwork StructureThe Triad CensusNetwork EvolutionVisualization and ClusteringChapter in Summary

Content preview from Data Mining Methods for the Content Analyst

DATA MINING METHODS FOR THECONTENT ANALYST

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 ...