LEADER 05700nam 2200709 450 001 9910465629503321 005 20200520144314.0 010 $a90-272-7034-1 035 $a(CKB)2560000000149325 035 $a(EBL)1676584 035 $a(SSID)ssj0001216308 035 $a(PQKBManifestationID)11792115 035 $a(PQKBTitleCode)TC0001216308 035 $a(PQKBWorkID)11190403 035 $a(PQKB)10222727 035 $a(MiAaPQ)EBC1676584 035 $a(Au-PeEL)EBL1676584 035 $a(CaPaEBR)ebr10861909 035 $a(OCoLC)878143295 035 $a(EXLCZ)992560000000149325 100 $a20140505h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aFrom text to political positions $etext analysis across disciplines /$fedited by Bertie Kaal, Isa Maks, Annemarie van Elfrinkhof 210 1$aAmsterdam, The Netherlands ;$aPhiladelphia, [Pennsylvania] :$cJohn Benjamins Publishing Company,$d2014. 210 4$dİ2014 215 $a1 online resource (345 p.) 225 1 $aDiscourse Approaches to Politics, Society and Culture ;$vv.55 300 $aDescription based upon print version of record. 311 $a90-272-0646-5 320 $aIncludes bibliographical references and index. 327 $aFrom Text to Political Positions; Editorial page; Title page; LCC data; Table of contents; Foreword; Reference; Acknowledgements; 1. Positions of parties and political cleavages between parties in texts; Political language and content analysis; Meta-language about political language; Three tools for analysing political texts; Does a concept occur? First-order agenda setting and entity recognition; The ontological problem: (named) entity recognition; Co-occurrence of concepts? Conditional probabilities and associative framing; Semantic network analysis; Manual coding using the NET-method 327 $aAutomation using semantic rules on top of an ontology, POS-tags, syntax dependency treesSummary; References; Part I. Computational methods for political text analysis; Introduction; 2. Comparing the position of Canadian political parties using French and English manifestos; Word-based parallel content analysis; Methodology; Canadian expert surveys; Wordscores; Wordfish; Conclusion; References; Appendix; 3. Leveraging textual sentiment analysis with social network modeling; 1. Introduction; 2. Data; 2.1 Election data; 2.2 Sentiment annotations; 3. Related work 327 $a4. Overview of the classification framework4.1 Shallow document classification; 4.2 Deep entity-level sentiment scoring; 4.3 Social network modeling; 4.4 Overview of algorithms; 5. Experiments; 5.1 Experimental conditions; 5.2 Evaluation measures; 5.3 Discussion; 5.4 Significance of results; 5.5 Future work; 6. Conclusion; References; 4. Issue framing and language use in the Swedish blogosphere; Introduction; The case of Sweden: Issue framing and the 'outsider' concept; Methodological considerations; Random indexing 327 $aLanguage use by the Social Democratic and the Conservative Moderate Party in relation to 'outsiders'The Conservative Moderate Party; The Social Democratic Party; From quality to quantity in party related documents; Random Indexing of words related to 'outsider' in the Swedish blogosphere 2008-2010; Summary and conclusions; References; Appendix; 5. Text to ideology or text to party status?; 1. Introduction; 2. Background: The Canadian party system and Parliament; 3. First set of experiments: Classifying by party; 3.1 Data; 3.2 Method; 3.3 Results; 3.4 Discussion 327 $a4. Second set of experiments: Classifying by party status4.1 Data; 4.2 Method and results; 4.3 Discussion; 5. Classification based on the emotional content of speeches; 5.1 Method and data; 5.2 Results; 6. Third set of experiments: European Parliamentary data; 6.1 Data; 6.2 Method; 6.3 Results; 6.4 Discussion; 7. Conclusion; References; 6. Sentiment analysis in parliamentary proceedings; 1. Introduction; 2. Background; 3. Data; 4. Assessing subjectivity and orientation; 4.1 Classification level; 4.2 Gold standard corpus; 4.3 Automatically determining subjectivity 327 $a4.4 Automatically determining semantic orientation 330 $aThis chapter explores how three methods of political text analysis can complement each other to differentiate parties in detail. A word-frequency method and corpus linguistic techniques are joined by critical discourse analysis in an attempt to assess the ideological relation between election manifestos and a coalition agreement. How does this agreement relate to the policy positions presented in individual election manifestos and whose issues appear on the governmental agenda? The chapter discusses the design of three levels of text analysis applying text-as-data analysis; words-as-meaningful 410 0$aDiscourse Approaches to Politics, Society and Culture 606 $aCommunication in politics 606 $aMass media$xPolitical aspects 606 $aPublic communication$xPolitical aspects 606 $aDiscourse analysis$xPolitical aspects 608 $aElectronic books. 615 0$aCommunication in politics. 615 0$aMass media$xPolitical aspects. 615 0$aPublic communication$xPolitical aspects. 615 0$aDiscourse analysis$xPolitical aspects. 676 $a401/.41 702 $aMaks$b Isa 702 $aKaal$b Bertie 702 $aElfrinkhof$b Annemarie van 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910465629503321 996 $aFrom text to political positions$92005267 997 $aUNINA