05700nam 2200709 450 991046562950332120200520144314.090-272-7034-1(CKB)2560000000149325(EBL)1676584(SSID)ssj0001216308(PQKBManifestationID)11792115(PQKBTitleCode)TC0001216308(PQKBWorkID)11190403(PQKB)10222727(MiAaPQ)EBC1676584(Au-PeEL)EBL1676584(CaPaEBR)ebr10861909(OCoLC)878143295(EXLCZ)99256000000014932520140505h20142014 uy 0engur|n|---|||||txtccrFrom text to political positions text analysis across disciplines /edited by Bertie Kaal, Isa Maks, Annemarie van ElfrinkhofAmsterdam, The Netherlands ;Philadelphia, [Pennsylvania] :John Benjamins Publishing Company,2014.©20141 online resource (345 p.)Discourse Approaches to Politics, Society and Culture ;v.55Description based upon print version of record.90-272-0646-5 Includes bibliographical references and index.From 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-methodAutomation 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 work4. 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 indexingLanguage 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 Discussion4. 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 subjectivity4.4 Automatically determining semantic orientationThis 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-meaningfulDiscourse Approaches to Politics, Society and CultureCommunication in politicsMass mediaPolitical aspectsPublic communicationPolitical aspectsDiscourse analysisPolitical aspectsElectronic books.Communication in politics.Mass mediaPolitical aspects.Public communicationPolitical aspects.Discourse analysisPolitical aspects.401/.41 Maks IsaKaal BertieElfrinkhof Annemarie vanMiAaPQMiAaPQMiAaPQBOOK9910465629503321From text to political positions2005267UNINA