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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
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    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.
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    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.

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    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

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