Abstract
AbstractThe 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 onBooking.com. Furthermore, iTb-NB is compared with Tb-NB in terms of model accuracy, resistance to noise, and computational efficiency.
Funder
Università degli Studi di Cagliari
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Cited by
1 articles.
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1. Iterative threshold-based Naïve bayes classifier;Statistical Methods & Applications;2023-09-05