LEADER 05569nam 2200709Ia 450 001 9910786303103321 005 20230801225608.0 010 $a0-203-14938-6 010 $a1-283-84361-7 010 $a1-136-51459-7 035 $a(CKB)2670000000298933 035 $a(EBL)1075229 035 $a(OCoLC)819136776 035 $a(SSID)ssj0000827298 035 $a(PQKBManifestationID)12314841 035 $a(PQKBTitleCode)TC0000827298 035 $a(PQKBWorkID)10820864 035 $a(PQKB)10990851 035 $a(MiAaPQ)EBC1075229 035 $a(WaSeSS)IndRDA00089311 035 $a(Au-PeEL)EBL1075229 035 $a(CaPaEBR)ebr10630770 035 $a(CaONFJC)MIL415611 035 $a(FINmELB)ELB134330 035 $a(EXLCZ)992670000000298933 100 $a20110719d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData mining methods for the content analyst$b[electronic resource] $ean introduction to the computational analysis of content /$fKalev Hannes Leetaru 210 $aNew York $cRoutledge$d2012 210 1$aNew York :$cRoutledge,$d2012. 215 $a1 online resource (121 p.) 225 0 $aRoutledge communication series 300 $aDescription based upon print version of record. 311 $a0-415-89514-6 311 $a0-415-89513-8 320 $aIncludes bibliographical references (p. [97]-99) and index. 327 $aDATA MINING METHODS FOR THE CONTENT ANALYST An Introduction to the Computational Analysis of Content; Copyright; Contents; List of Tables and Figures; Acknowledgments; 1 Introduction; What Is Content Analysis?; Why Use Computerized Analysis Techniques?; Standalone Tools or Integrated Suites; Transitioning from Theory to Practice; Chapter in Summary; 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 327 $aSources of IncompletenessLicensing 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 in Summary; 3 Vocabulary Analysis; The Basics; Word Histograms; Readability Indexes; Normative Comparison 327 $aNon-word AnalysisColloquialisms: 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 in Summary; 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 in Summary; 5 Lexicons, Entity Extraction, and Geocoding 327 $aLexiconsLexicons 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 in Summary; 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 327 $aMultilingual Analysis: Topic Extraction with Multiple LanguagesChapter in Summary; 7 Sentiment Analysis; Examining Emotions; Evolution; Evaluation; Analytical Resolution: Documents versus Objects; Hand-crafted versus Automatically Generated Lexicons; Other Sentiment Scales; Limitations; Measuring Language Rather Than Worldview; Chapter in Summary; 8 Similarity, Categorization and Clustering; Categorization; The Vector Space Model; Feature Selection; Feature Reduction; Learning Algorithm; Evaluating ATC Results; Benefi ts of ATC over Human Categorization; Limitations of ATC 327 $aApplications of ATC 330 $aWith continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike.Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data m 410 0$aRoutledge Communication Series 606 $aData mining 606 $aData mining$xStatistical methods 615 0$aData mining. 615 0$aData mining$xStatistical methods. 676 $a006.3/12 676 $a006.312 700 $aLeetaru$b Kalev$01537791 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910786303103321 996 $aData mining methods for the content analyst$93787317 997 $aUNINA