From text to political positions : text analysis across disciplines / edited by Bertie Kaal, Isa Maks, Anneie van Elfrinkhof.

Online resource; title from pdf information screen (Ebsco, viewed April 25, 2014).

Includes bibliographical references and index.

This 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.

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-method Automation using semantic rules on top of an ontology, POS-tags, syntax dependency trees; Summary ; 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 4. Overview of the classification framework ; 4.1 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.

Language 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.

4. Second set of experiments: Classifying by party status 4.1 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.

eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - Worldwide