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  1. Text mining methods for measuring the coherence of party manifestos for the German federal elections from 1990 to 2021
    Erschienen: [2021]
    Verlag:  Dortmund Center for Data-Based Media Analysis, [Dortmund]

    Text mining is an active field of statistical research. In this paper we use two methods from text mining: the Poisson Reduced Rank Model (PRR, see Jentsch et al. 2020; Jentsch et al. 2021) and the Latent Dirichlet Allocation model (LDA, see Blei et... mehr

    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    DS 737
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    Text mining is an active field of statistical research. In this paper we use two methods from text mining: the Poisson Reduced Rank Model (PRR, see Jentsch et al. 2020; Jentsch et al. 2021) and the Latent Dirichlet Allocation model (LDA, see Blei et al. 2003) for the statistical analysis of party manifesto texts from Germany. For the nine federal elections in Germany from 1990 to 2021, we analyze party manifestos that have been written by the parties to present their political positions and goals for the next legislative period of the German federal parliament (Bundestag). We use the models to quantify distances in the language of the manifestos and in the weight of importance the parties attribute to several political topics. The statistical analysis is purely data driven. No outside information, e.g., on the position of the parties, on the meaning of words, or on currently hot political topics, is used in fitting the statistical models. Outside information is only used when we interpret the statistical results.

     

    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 2003/40491
    Schriftenreihe: DoCMA working paper ; # 8 (September 2021)
    Schlagworte: Poisson reduced-rank model; Latent Dirichlet Allocation; CDU; CSU; Union; SPD; Grüne; FDP; Linke; Kenia; Jamaica; Ampel; Deutschland; R2G; coalition
    Umfang: 1 Online-Ressource (circa 23 Seiten), Illustrationen