Evolution of the Perceptions about Tourist Destinations Affected by Risk Events Using a PANAS-tDL Deep Learning Model

Abstract

The tourism industry has dynamized the economy of the countries by offering places, as well as related tourism experiences, products, and services. In the context of the COVID-19 pandemic, some of these tourist destinations were affected by subjective perceptions of users on social networks, within stands out Twitter. To achieve an objective perception from user comments posted on Twitter in front of a tourist destination, we propose a PANAS-tDL (Positive and Negative Affect Schedule -Deep Learning) model which integrates into a single structure a neural model inspired by a Stacked neural deep learning model (SDL), as well as the PANAS-t methodology. For this process, a database of comments was available for four destinations (Colombia, Italy, Spain, USA), and its tourist's products and services, before and in the context of COVID-19 pandemic throughout the year 2020. The proposed model made it possible to generate objective perceptions of the tourist destinations and their products and services using an automatic classification of comments in each category defined by the PANAS-t methodology (11-sentiments). The results show how users' perceptions were towards the negative sentiment zone defined by this methodology, according to the evolution of the COVID-19 pandemic worldwide throughout the year 2020. The proposed model also integrated an automatic process of normalisation, lemmatisation and tokenisation (Natural language process -NLP) for the objective characterization of perceptions, and due to its capacity for adaption and learning, it can be extended for the evaluation of new tourist destinations, products or services using comments from different social networks.