Tourism Management Through Natural Language Processing and Sentiment Analysis. A Case Study of the Main Natural Areas of Extremadura, Spain

Nowadays, digital scenarios and global connectivity have changed the way we travel; visitors can comment and share their experiences from different sources of information, and this action can be of essential value in tourism research. Consequently, these factors can help design new marketing, management, or business strategies and make tourist destinations more competitive. Multiple techniques can process big data and machine learning analysis, such as natural language processing (NLP), which can support tourist resources in analyzing this information. This paper proposes an NLP technique to present an emotion detection analysis based on social web reviews about the main natural spaces in Extremadura, Spain. Sentiment and emotions scores have been computed based on the Plutchik model using a lexicon database to understand visitor experience. Empirical evidence suggests some recommendations regarding tourism management to improve the visitor experience and reduce negative sentiments and emotions.