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Iterative threshold-based Naïve bayes classifier

Author

Listed:
  • Maurizio Romano

    (University of Cagliari)

  • Gianpaolo Zammarchi

    (University of Cagliari)

  • Claudio Conversano

    (University of Cagliari)

Abstract

The iterative Threshold-based Naïve Bayes (iTb-NB) classifier is introduced as a (simple) improved version of the previously introduced non-iterative Threshold-based Naïve Bayes (Tb-NB) classifier. iTb-NB starts from a Natural Language text-corpus and allows the user to quantify with a numeric value a sentiment (positive or negative) from a specific test. Differently from Tb-NB, iTb-NB is an algorithm aimed at estimating multiple threshold values that concur to refine Tb-NB’s decision rules when classifying a text into positive (negative) based on its content. Observations with sentiment scores close to the threshold are marked to be reclassified, hence a new decision rule is defined for them. Such “iterative” process improves the quality of predictions w.r.t. Tb-NB but keeping the possibility to utilize its results as the input of useful post-hoc analyses. The effectiveness of iTb-NB is evaluated analyzing hotel guests’ reviews from all hotels located in the Sardinia region and available on Booking.com. Furthermore, iTb-NB is compared with Tb-NB in terms of model accuracy, resistance to noise, and computational efficiency.

Suggested Citation

  • Maurizio Romano & Gianpaolo Zammarchi & Claudio Conversano, 2024. "Iterative threshold-based Naïve bayes classifier," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(1), pages 235-265, March.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:1:d:10.1007_s10260-023-00721-1
    DOI: 10.1007/s10260-023-00721-1
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