Iterative threshold-based Naive bayes classifier

被引：1
|
作者：
Romano, Maurizio [1 ]
Zammarchi, Gianpaolo [1 ]
Conversano, Claudio [1 ]
机构：
[1] Univ Cagliari, Dept Econ & Business Sci, Viale Fra Ignazio 17, I-09123 Cagliari, Italy
来源：
STATISTICAL METHODS AND APPLICATIONS | 2024年 / 33卷 / 01期
关键词：
Naive bayes; Post-hoc analysis; Customer satisfaction; Sentiment analysis; Natural language processing; Booking.com; SENTIMENT ANALYSIS; REVIEWS;
D O I：
10.1007/s10260-023-00721-1
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
The iterative Threshold-based Naive Bayes (iTb-NB) classifier is introduced as a (simple) improved version of the previously introduced non-iterative Threshold-based Naive 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.
引用
收藏
页码：235 / 265
页数：31
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