Text Mining of Airbnb Reviews: A holistic approach on reviewers’ opinions and topics distribution

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceThis thesis aims to perform a holistic investigation concerning how Airbnb accommodation features and hosts’ attributes influence guest’s reviews and how are the main topics distributed. A dataset containing almost 4 million reviews from major touristic cities in the world (Milan, Lisbon, Amsterdam, Toronto, San-Francisco, and Sydney) was used for the text mining analysis to uncover the reviews’ social and market norms, as well as the guests’ sentiments and topics distribution. This research uses both Mallet LDA (Latent Dirichlet Allocation) and Word2Vec methods to unveil the semantic structure and similarity between data in this study. This approach will allow hospitality providers to understand the impact of underlying factors on reviewers’ opinions for further improvement of their services. Finally, this study develops a predictive unbiased model to forecast the review’s scores, with an accuracy of 90.70%