Iterative Threshold-based Naïve Bayes Classifier: an efficient Tb-NB improvement

Romano, Maurizio
Primo
;Zammarchi, Gianpaolo
Secondo
;Contu, Giulia
Ultimo
2022-01-01

Abstract

While analyzing online reviews on Booking.com, we proposed an ad-hoc classification model (Threshold-based Naïve Bayes Classifier, Tb-NB) to evaluate Customer Satisfaction, starting from the reviews' content, and predicting them as positive/negative. The log-likelihood ratios attributed to each word included in a review are then used to estimate a numeric sentiment score. In this paper we propose an improved version of Tb-NB called "iterative" Tb-NB. It results in a second step of Tb-NB: starting from the output of Tb-NB and reclassifying reviews with a probabilistic approach, it refines iteratively the threshold value used to classify a given subset of reviews.
2022
Inglese
SIS 2022 Book of the short papers
9788891932310
Pearson
1656
1661
6
SIS 2022
Esperti anonimi
22-24 Giugno 2022
Caserta
internazionale
scientifica
Threshold-based Naïve Bayes Classifier, Iterative Threshold-based Naïve Bayes Classifier, Customer Satisfaction, Sentiment Analysis, General Sentiment Decomposition
no
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Romano, Maurizio; Zammarchi, Gianpaolo; Contu, Giulia
273
3
4.1 Contributo in Atti di convegno
none
info:eu-repo/semantics/conferencePaper
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Questionario e social

Condividi su:
Impostazioni cookie