Predicting Sentiment Analysis in Home Holiday Rentals: A Portuguese Experience - Almeida, D.; Laureano, Raul M. S.; Cruz, F.; Laureano, L. (TMS ALGARVE 2022, 2022)
Portugal has been, for many years, an attractive destination for tourists from all over the world. This continuous flow of people opens opportunities for companies to explore and for some new other companies to emerge. All the data generated from the interaction of these companies with tourists can be submitted to data mining techniques to extract useful information and, therefore, create knowledge. This case study uses decision trees to predict the polarity of sentiments found in the online reviews of the properties that a Portuguese accommodation holiday rental platform manages. A sample of 1131 reservation Out of the Feels Like Home’s portfolio, information regarding negative and positive mentions for each house (monthly) was retrieved from ReviewPro’s API, allowing for the final data set contain important information to be targeted by data mining. Through the usage of descriptive analysis and predictive models (decision trees), the main properties and reservations’ characteristics that can help to predict the sentiment polarity found in the reviews are revealed. This way, this study generates useful knowledge for Feels Like Home and possibly for the rest of the industry to use and adapt to their business needs.