Demand Forecasting and Opinion Mining to Optimize Hotel Profit and Organization

In this short course I will review how hotel reservation systems work, including explanation of the reservation channels. These reservation-based data provides large amounts of information that allow us to optimize the hotel processes, especially the optimization of revenue. I will explain the analytics behind hotel revenue optimization. For successful revenue optimization, an accurate forecast of incoming reservations and occupancy is needed. I will review the major time series forecasting approaches, as applied to hotel data.

Hotel reviews on reservation sites are very informative for the guest, and guide him towards better hotel selection. Moreover, they give the hotel feedback that helps him to improve his services. Machine learning models have been introduced for automatic analysis of the reviews, leading to the so-called opinion mining concept. I will explain the concept, and provide an overview of machine learning models for opinion mining.

Course leader

Professor Amir Atiya, Cairo University

Target group

PhD students, practitioners and business participants in tourism and hospitality.

Course aim

Understanding the state of the art in forecasting and opinion mining for the hospitality industry