IDEAS home Printed from https://ideas.repec.org/p/men/wpaper/11_2011.html
   My bibliography  Save this paper

Approaches to samples selection for machine learning based classification of textual data

Author

Listed:
  • Frantisek Darena

    (Department of Informatics, Faculty of Business and Economics, Mendel University in Brno)

  • Jan Zizka

    (Department of Informatics, Faculty of Business and Economics, Mendel University in Brno)

Abstract

The paper focuses on retrieval of relevant documents written in a natural language based on availability of several candidate examples which are used as the basis for the automatic selection of only items that are similar to these predefined patterns. Presented approach should face problems related to processing user created content in natural language that include a poor control over the topic and the structure of the content and often also huge computational complexity. Three methods of selecting the best samples from a large set of candidate samples are presented - random selection, manual selection and a new approach called automatic biased sample selection, and measures based on Euclidean distance and cosine similarity are used for classification. The experiments are carried out with real world data consisting of customer reviews downloaded from amazon.com, converted to different representations based on bag-of-words procedure. The experiments and the results of the presented approach provided satisfactory values and can lead to an alternative approach to manual selection and evaluation of textual samples.

Suggested Citation

  • Frantisek Darena & Jan Zizka, 2011. "Approaches to samples selection for machine learning based classification of textual data," MENDELU Working Papers in Business and Economics 2011-11, Mendel University in Brno, Faculty of Business and Economics.
    • Handle: RePEc:men:wpaper:11_2011
    as

    Download full text from publisher

    File URL: http://ftp.mendelu.cz/RePEc/men/wpaper/11_2011.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
    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. Frantisek Darena & Jan Zizka, 2016. "Ensembles of Classifiers for Parallel Categorization of Large Number of Text Documents Expressing Opinions," MENDELU Working Papers in Business and Economics 2016-65, Mendel University in Brno, Faculty of Business and Economics.

    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. Simon Hug & Tobias Schulz, 2007. "Referendums in the EU’s constitution building process," The Review of International Organizations, Springer, vol. 2(2), pages 177-218, June.
    2. Yang, Chao & Huang, Cui, 2022. "Quantitative mapping of the evolution of AI policy distribution, targets and focuses over three decades in China," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Ralf Meinhardt & Sebastian Junge & Martin Weiss, 2018. "The organizational environment with its measures, antecedents, and consequences: a review and research agenda," Management Review Quarterly, Springer, vol. 68(2), pages 195-235, April.
    4. Torsten J. Selck, 2005. "Improving the Explanatory Power of Bargaining Models," Journal of Theoretical Politics, , vol. 17(3), pages 371-375, July.
    5. Cory Koedel & Jiaxi Li & Matthew G. Springer & Li Tan, 2018. "Teacher Performance Ratings and Professional Improvement," Working Papers 1808, Department of Economics, University of Missouri.
    6. Huang, Cui & Yang, Chao & Su, Jun, 2021. "Identifying core policy instruments based on structural holes: A case study of China’s nuclear energy policy," Journal of Informetrics, Elsevier, vol. 15(2).
    7. Sarel, Roee & Demirtas, Melanie, 2021. "Delegation in a multi-tier court system: Are remands in the U.S. federal courts driven by moral hazard?," European Journal of Political Economy, Elsevier, vol. 68(C).
    8. Yu, Feifei & Wang, Liting & Li, Xiaotong, 2020. "The effects of government subsidies on new energy vehicle enterprises: The moderating role of intelligent transformation," Energy Policy, Elsevier, vol. 141(C).
    9. Pierre-Marc Daigneault & Dominic Duval & Louis M. Imbeau, 2018. "Supervised scaling of semi-structured interview transcripts to characterize the ideology of a social policy reform," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2151-2162, September.
    10. Maria Stella Righettini, 2021. "Framing Sustainability. Evidence from Participatory Forums to Taylor the Regional 2030 Agenda to Local Contexts," Sustainability, MDPI, vol. 13(8), pages 1-15, April.
    11. Hayo, Bernd & Henseler, Kai & Steffen Rapp, Marc & Zahner, Johannes, 2022. "Complexity of ECB communication and financial market trading," Journal of International Money and Finance, Elsevier, vol. 128(C).
    12. Protte, Benjamin, 2012. "Does Fleet Street shape politics? Estimating the Effect of Newspaper Coverage about Globalization on the Support for Unemployment Insurance," Working Papers 12-19, University of Mannheim, Department of Economics.
    13. 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.
    14. Kenneth Benoit & Michael Laver, 2005. "Mapping the Irish Policy Space - Voter and Party Spaces in Preferential Elections," The Economic and Social Review, Economic and Social Studies, vol. 36(2), pages 83-108.
    15. McCullough, Ellen B., 2017. "Labor productivity and employment gaps in Sub-Saharan Africa," Food Policy, Elsevier, vol. 67(C), pages 133-152.
    16. Harris, Colin & Myers, Andrew & Kaiser, Adam, 2023. "The humanizing effect of market interaction," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 489-507.
    17. Seraphine F. Maerz & Carsten Q. Schneider, 2020. "Comparing public communication in democracies and autocracies: automated text analyses of speeches by heads of government," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(2), pages 517-545, April.
    18. Hayo, Bernd & Henseler, Kai & Rapp, Marc Steffen, 2019. "Estimating the monetary policy interest-rate-to-performance sensitivity of the European banking sector at the zero lower bound," Finance Research Letters, Elsevier, vol. 31(C).
    19. Baerg, Nicole Rae, 2014. "War of the Words: How Elites' Communication Changes the Economy," MPRA Paper 59823, University Library of Munich, Germany.

    More about this item

    Keywords

    text classification; textual patterns; machine learning; natural language processing; text similarity;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

    Statistics

    Access and download statistics

    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:men:wpaper:11_2011. 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: Luděk Kouba (email available below). General contact details of provider: https://edirc.repec.org/data/femencz.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.