The purpose of this paper is to show the application of a set of intelligent data analysis techniques to about 7 million of online travel reviews, with the aim of automatically extracting useful information. The reviews, collected from two popular online tourism-related review platforms, are all those posted by reviewers about one specific Italian location, from 2010 to 2017. To carry out the study, the following methodology is applied: a preliminary statistical analysis is performed to acquire general knowledge about the datasets, such as the geographical distribution of reviewers, their activities, and a comparison among the time of visits and the average scores of the reviews. Then, Natural Language Processing techniques are applied to extract and compare the most frequent words used in the two platforms. Finally, an Association Rule Learning algorithm is applied, to extract preferred destinations for distinct groups of reviewers, by mining interesting associations among the countries of origin of the reviewers and the most frequent destinations visited. By elaborating the available data, it is possible to automatically disclose valuable information for consumers and providers. The information automatically extracted can be exploited, for example, to build a recommender system for customers or a market analysis tool for service providers.
A study on online travel reviews through intelligent data analysis
Fazzolari M;Petrocchi M
2018
Abstract
The purpose of this paper is to show the application of a set of intelligent data analysis techniques to about 7 million of online travel reviews, with the aim of automatically extracting useful information. The reviews, collected from two popular online tourism-related review platforms, are all those posted by reviewers about one specific Italian location, from 2010 to 2017. To carry out the study, the following methodology is applied: a preliminary statistical analysis is performed to acquire general knowledge about the datasets, such as the geographical distribution of reviewers, their activities, and a comparison among the time of visits and the average scores of the reviews. Then, Natural Language Processing techniques are applied to extract and compare the most frequent words used in the two platforms. Finally, an Association Rule Learning algorithm is applied, to extract preferred destinations for distinct groups of reviewers, by mining interesting associations among the countries of origin of the reviewers and the most frequent destinations visited. By elaborating the available data, it is possible to automatically disclose valuable information for consumers and providers. The information automatically extracted can be exploited, for example, to build a recommender system for customers or a market analysis tool for service providers.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
