Online reviews have been found very useful in decision-making. It is important to design and implement accurate systems to analyze the reviews and, based on textual information, predict their ratings. Given the different sources, languages and evaluating systems, intelligent systems are needed to use textual and numerical reviews to better understand the evaluation of the tourist experience and derive useful information to improve the offer. This paper aims to present an eXplainable Artificial Intelligence framework that contributes to the discussion on numerical and textual evaluations of the hospitality experience. It combines sentiment analysis and machine learning to accurately model and explain the evaluation of the tourist experience. The main findings are that review ratings should be used with caution and accompanied by a sentiment evaluation and explainability plays a central role in identifying which are the key concepts of positive or negative ratings, providing invaluable intelligence about the tourist experience.
The verbalization of numbers: An explainable framework for tourism online reviews
Bellantuono, Loredana;Cilli, Roberto;Lombardi, Angela;Pantaleo, Ester;Petruzzellis, Luca
;Tangaro, Sabina;Monaco, Alfonso;Amoroso, Nicola;Bellotti, Roberto
2023-01-01
Abstract
Online reviews have been found very useful in decision-making. It is important to design and implement accurate systems to analyze the reviews and, based on textual information, predict their ratings. Given the different sources, languages and evaluating systems, intelligent systems are needed to use textual and numerical reviews to better understand the evaluation of the tourist experience and derive useful information to improve the offer. This paper aims to present an eXplainable Artificial Intelligence framework that contributes to the discussion on numerical and textual evaluations of the hospitality experience. It combines sentiment analysis and machine learning to accurately model and explain the evaluation of the tourist experience. The main findings are that review ratings should be used with caution and accompanied by a sentiment evaluation and explainability plays a central role in identifying which are the key concepts of positive or negative ratings, providing invaluable intelligence about the tourist experience.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
