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Autores
Resumo(s)
This study investigates user-generated reviews in the high-end hotel industry in the city of Lisbon, London, and Paris to identify important attributes to guests and the sentiment behind them. The goals are to discover if the COVID-19 pandemic affected guest priorities, expectations, and perceptions before, during, and after it. In total 222,679 reviews from 281 five-star hotels were retrieved, organized, and analyzed through different text mining techniques and sentiment analysis. The results show that during that because of the pandemic, guests started prioritizing more the quality of service and interaction they had with the hotel staff over any other attributes. When comparing the three distinct locations guests had similar changes in priority. Insights from this study can help hotel managers better plan and prepare their services during and after pandemics.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing Intelligence
Palavras-chave
Pandemic COVID-19 user-generated content reviews hotels tourism text mining sentiment analysis competitive importance analysis Lisbon Paris London
