Abstract
AbstractThe Threshold-based Naïve Bayes (Tb-NB) classifier is introduced as a (simple) improved version of the original Naïve Bayes classifier. Tb-NB extracts the sentiment from a Natural Language text corpus and allows the user not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. It is based on the estimation of a single threshold value that concurs to define a decision rule that classifies a text into a positive (negative) opinion based on its content. One of the main advantage deriving from Tb-NB is the possibility to utilize its results as the input of post-hoc analysis aimed at observing how the quality associated to the different dimensions of a product or a service or, in a mirrored fashion, the different dimensions of customer satisfaction evolve in time or change with respect to different locations. The effectiveness of Tb-NB is evaluated analyzing data concerning the tourism industry and, specifically, hotel guests’ reviews from all hotels located in the Sardinian region and available on Booking.com. Moreover, Tb-NB is compared with other popular classifiers used in sentiment analysis in terms of model accuracy, resistance to noise and computational efficiency.
Funder
Ministry of University (IT) - Prog. Dipartimenti di Eccellenza
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Statistics and Probability
Reference41 articles.
1. Arndt J (1967) Role of product-related conversations in the diffusion of a new product. J Market Res 4(3):291–295. https://doi.org/10.2307/3149462
2. Bachtiar FA, Paulina W, Rusydi AN (2020) Text mining for aspect based sentiment analysis on customer review: a case study in the hotel industry. In: Serdült U, Loshchilov A, Mahmudy WF, Nurwasito H (eds) Proceedings of the 5th international workshop on innovations in information and communication science and technology (canceled by authorities due to SARS-CoV-2), CEUR workshop proceedings, vol 2627, pp 105–112, Malang, Indonesia, CEUR-WS.org
3. Boyd D, Crawford K (2012) Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf Commun Soc 15(5):662–679. https://doi.org/10.1080/1369118X.2012.678878
4. Brownlee J (2017) Deep learning for natural language processing: develop deep learning models for your natural language problems. In: Machine learning mastery, 1.7 edition
5. Buttle FA (1998) Word of mouth: understanding and managing referral marketing. J Strateg Market 6(3):241–254. https://doi.org/10.1080/096525498346658
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