Iterative threshold-based Naïve bayes classifier

Maurizio Romano (), Gianpaolo Zammarchi () and Claudio Conversano ()
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Maurizio Romano: University of Cagliari
Gianpaolo Zammarchi: University of Cagliari
Claudio Conversano: University of Cagliari

Statistical Methods & Applications, 2024, vol. 33, issue 1, No 9, 235-265

Abstract: 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.

Keywords: Naïve bayes; Post-hoc analysis; Customer satisfaction; Sentiment analysis; Natural language processing; Booking.com (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10260-023-00721-1

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