IDEAS home Printed from https://ideas.repec.org/a/zbw/espost/180851.html
   My bibliography  Save this article

Validating a sentiment dictionary for German political language—a workbench note

Author

Listed:
  • Rauh, Christian

Abstract

Automated sentiment scoring offers relevant empirical information for many political science applications. However, apart from English language resources, validated dictionaries are rare. This note introduces a German sentiment dictionary and assesses its performance against human intuition in parliamentary speeches, party manifestos, and media coverage. The tool published with this note is indeed able to discriminate positive and negative political language. But the validation exercises indicate that positive language is easier to detect than negative language, while the scores are numerically biased to zero. This warrants caution when interpreting sentiment scores as interval or even ratio scales in applied research.

Suggested Citation

  • Rauh, Christian, 2018. "Validating a sentiment dictionary for German political language—a workbench note," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(4), pages 319-343.
    • Handle: RePEc:zbw:espost:180851
    DOI: 10.1080/19331681.2018.1485608
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/180851/3/f-21403-full-text-Rauh-Validating-v3.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.1080/19331681.2018.1485608?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Mikhaylov, Slava & Laver, Michael & Benoit, Kenneth R., 2012. "Coder Reliability and Misclassification in the Human Coding of Party Manifestos," Political Analysis, Cambridge University Press, vol. 20(1), pages 78-91, January.
    3. Merz, Nicolas & Regel, Sven & Lewandowski, Jirka, 2016. "The Manifesto Corpus: A new resource for research on political parties and quantitative text analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 3(2 (April-), pages 1-8.
    4. Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
    5. Kenneth Benoit & Michael Laver & Slava Mikhaylov, 2009. "Treating Words as Data with Error: Uncertainty in Text Statements of Policy Positions," American Journal of Political Science, John Wiley & Sons, vol. 53(2), pages 495-513, April.
    6. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Garz, Marcel & Sörensen, Jil & Stone, Daniel F., 2020. "Partisan selective engagement: Evidence from Facebook," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 91-108.
    2. Koop, Christel & Scotto di Vettimo, Michele, 2023. "How do the media scrutinise central banking? Evidence from the Bank of England," European Journal of Political Economy, Elsevier, vol. 77(C).
    3. Ozgun, Burcu & Broekel, Tom, 2021. "The geography of innovation and technology news - An empirical study of the German news media," Technological Forecasting and Social Change, Elsevier, vol. 167(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jae Yeon Kim, 2021. "Integrating human and machine coding to measure political issues in ethnic newspaper articles," Journal of Computational Social Science, Springer, vol. 4(2), pages 585-612, November.
    2. Kostas Gemenis, 2015. "An iterative expert survey approach for estimating parties’ policy positions," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2291-2306, November.
    3. Enriqueta Aragonès & Dimitrios Xefteris, 2017. "Imperfectly Informed Voters And Strategic Extremism," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 58(2), pages 439-471, May.
    4. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
    5. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    6. Aritz Bilbao-Jayo & Aitor Almeida, 2018. "Automatic political discourse analysis with multi-scale convolutional neural networks and contextual data," International Journal of Distributed Sensor Networks, , vol. 14(11), pages 15501477188, November.
    7. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.
    8. Eyal Eckhaus & Zachary Sheaffer, 2018. "Managerial hubris detection: the case of Enron," Risk Management, Palgrave Macmillan, vol. 20(4), pages 304-325, November.
    9. Osterloh, Steffen, 2012. "Words speak louder than actions: The impact of politics on economic performance," Journal of Comparative Economics, Elsevier, vol. 40(3), pages 318-336.
    10. Angela Chang & Peter J. Schulz & Angus Wenghin Cheong, 2020. "Online Newspaper Framing of Non-Communicable Diseases: Comparison of Mainland China, Taiwan, Hong Kong and Macao," IJERPH, MDPI, vol. 17(15), pages 1-15, August.
    11. Heike Klüver, 2015. "The promises of quantitative text analysis in interest group research: A reply to Bunea and Ibenskas," European Union Politics, , vol. 16(3), pages 456-466, September.
    12. Wolfinger, Julia & Köhler, Ekkehard A. & Feld, Lars P. & Thomas, Tobias, 2018. "57 Channels (And Nothin On): Does TV-News on the Eurozone affect Government Bond Yield Spreads?," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181610, Verein für Socialpolitik / German Economic Association.
    13. Lehotský, Lukáš & Černoch, Filip & Osička, Jan & Ocelík, Petr, 2019. "When climate change is missing: Media discourse on coal mining in the Czech Republic," Energy Policy, Elsevier, vol. 129(C), pages 774-786.
    14. Caroline Le Pennec, 2020. "Strategic Campaign Communication: Evidence from 30,000 Candidate Manifestos," SoDa Laboratories Working Paper Series 2020-05, Monash University, SoDa Laboratories.
    15. Sara Kahn-Nisser, 2019. "When the targets are members and donors: Analyzing inter-governmental organizations’ human rights shaming," The Review of International Organizations, Springer, vol. 14(3), pages 431-451, September.
    16. Laura K. Nelson & Derek Burk & Marcel Knudsen & Leslie McCall, 2021. "The Future of Coding: A Comparison of Hand-Coding and Three Types of Computer-Assisted Text Analysis Methods," Sociological Methods & Research, , vol. 50(1), pages 202-237, February.
    17. André Krouwel & Annemarie Elfrinkhof, 2014. "Combining strengths of methods of party positioning to counter their weaknesses: the development of a new methodology to calibrate parties on issues and ideological dimensions," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1455-1472, May.
    18. Ralf Dewenter & Uwe Dulleck & Tobias Thomas, 2020. "Does the 4th estate deliver? The Political Coverage Index and its application to media capture," Constitutional Political Economy, Springer, vol. 31(3), pages 292-328, September.
    19. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
    20. Merz, Nicolas & Regel, Sven & Lewandowski, Jirka, 2016. "The Manifesto Corpus: A new resource for research on political parties and quantitative text analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 3(2 (April-), pages 1-8.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:espost:180851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.