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

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REFERENCES

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Brill, E. (1992). “A simple rule-based part of speech tagger,” Proceedings of the Third Conference on Applied Natural Language Processing, Trento, Italy, 152–155.

Ceglowski, M., Coburn, A. and Cuadrado, J. (2003). Semantic Search of Unstructured Data using Contextual Network Graphs, Preliminary white paper, National Institute for Technology and Liberal Education, Middlebury ...