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Simple and efficient classification scheme based on specific vocabulary

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  • Jacques Savoy
  • Olena Zubaryeva

Abstract

Assuming a binomial distribution for word occurrence, we propose computing a standardized Z score to define the specific vocabulary of a subset compared to that of the entire corpus. This approach is applied to weight terms (character n-gram, word, stem, lemma or sequence of them) which characterize a document. We then show how these Z score values can be used to derive a simple and efficient categorization scheme. To evaluate this proposition and demonstrate its effectiveness, we develop two experiments. First, the system must categorize speeches given by B. Obama as being either electoral or presidential speech. In a second experiment, sentences are extracted from these speeches and then categorized under the headings electoral or presidential. Based on these evaluations, the proposed classification scheme tends to perform better than a support vector machine model for both experiments, on the one hand, and on the other, shows a better performance level than a Naïve Bayes classifier on the first test and a slightly lower performance on the second (10-fold cross validation). Copyright Springer-Verlag 2012

Suggested Citation

  • Jacques Savoy & Olena Zubaryeva, 2012. "Simple and efficient classification scheme based on specific vocabulary," Computational Management Science, Springer, vol. 9(3), pages 401-415, August.
    • Handle: RePEc:spr:comgts:v:9:y:2012:i:3:p:401-415
    DOI: 10.1007/s10287-012-0149-z
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    References listed on IDEAS

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    1. Rong Zheng & Jiexun Li & Hsinchun Chen & Zan Huang, 2006. "A framework for authorship identification of online messages: Writing‐style features and classification techniques," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 378-393, February.
    2. Claire Fautsch & Jacques Savoy, 2009. "Algorithmic stemmers or morphological analysis? An evaluation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1616-1624, August.
    3. Aidan Finn & Nicholas Kushmerick, 2006. "Learning to classify documents according to genre," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(11), pages 1506-1518, September.
    4. Efstathios Stamatatos, 2009. "A survey of modern authorship attribution methods," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 538-556, March.
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