Detection of visitor expectations in the tourism industry using sentiment analysis during the COVID pandemic

Abstract

"The coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact on the hotel industry, causing serious social and financial risks. The COVID-19 pandemic has had a negative impact on the hotel industry’s hospitality and challenged travel across the globe. The COVID-19 pandemic was between December 2019 and March 2021. Using sentiment analysis and topic modelling on client feedback regarding the hospitality offered by hotels during this time period in various countries, this investigation intends to identify customer satisfaction. Using sentiment analysis, it is possible to categorize customer satisfaction in a number of different ways. We developed improved parameters. Topic modelling is used to understand the various topics that customers discuss the most. We discovered that Indonesia and America are capable of meeting customer expectations. Sri Lanka performed well in Asia. We found that the top 14 topics that people were talking about were overall service, staff, cleanliness, room, slow booking, and hotel response to a pandemic. For topic modelling, we have selected simple LDA and LDA Mallet models. The LDA model has a coherence score of 0.35, whereas the LDA Mallet model has a coherence score of 0.49, which shows that the Mallet model separated the topics much better. So the LDA Mallet model is performing better than the normal LDA model. Senior hotel managers in developed and developing nations will benefit from the study's findings as they work to introduce innovative services that will please patrons and win back their trust."