bunea, adriana ibenskas, raimondas and s binderkrantz, anne 2016. estimating interest groups’ policy positions through content analysis: a discussion of automated and human-coding text analysis techniques applied to studies of EU lobbying. European Political Science,
Iliev, Rumen and Smirnova, Anastasia 2016. Revealing Word Order: Using Serial Position in Binomials to Predict Properties of the Speaker. Journal of Psycholinguistic Research, Vol. 45, Issue. 2, p. 205.
Dehghani, Morteza Sagae, Kenji Sachdeva, Sonya and Gratch, Jonathan 2014. Analyzing Political Rhetoric in Conservative and Liberal Weblogs Related to the Construction of the “Ground Zero Mosque”. Journal of Information Technology & Politics, Vol. 11, Issue. 1, p. 1.
Przybyła, Piotr and Teisseyre, Paweł 2014. Analysing Utterances in Polish Parliament to Predict Speaker’s Background. Journal of Quantitative Linguistics, Vol. 21, Issue. 4, p. 350.
Soroka, Stuart 2014. Communication and Language Analysis in the Corporate World.
Verberne, Suzan D’hondt, Eva van den Bosch, Antal and Marx, Maarten 2014. Automatic thematic classification of election manifestos. Information Processing & Management, Vol. 50, Issue. 4, p. 554.
Grimmer, Justin 2013. Comment. Journal of the American Statistical Association, Vol. 108, Issue. 503, p. 770.
Meyer, Thomas M. and Jenny, Marcelo 2013. Measuring error for adjacent policy position estimates: Dealing with uncertainty using CMP data. Electoral Studies, Vol. 32, Issue. 1, p. 174.
Dahllof, M. 2012. Automatic prediction of gender, political affiliation, and age in Swedish politicians from the wording of their speeches--A comparative study of classifiability. Literary and Linguistic Computing, Vol. 27, Issue. 2, p. 139.
Legislative speech records from the 101st to 108th Congresses of the US Senate are analysed to study political ideologies. A widely-used text classification algorithm – Support Vector Machines (SVM) – allows the extraction of terms that are most indicative of conservative and liberal positions in legislative speeches and the prediction of senators’ ideological positions, with a 92 per cent level of accuracy. Feature analysis identifies the terms associated with conservative and liberal ideologies. The results demonstrate that cultural references appear more important than economic references in distinguishing conservative from liberal congressional speeches, calling into question the common economic interpretation of ideological differences in the US Congress.
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