  There has been significant literature regarding the way product review
sentiment affects brand loyalty. Intrigued by how natural language influences
consumer choice, we were motivated to examine whether an AirBnb host's
occupancy rate (how often their listing is booked out of the days they
indicated their listing was available) can be determined by the perceived
sentiment and length of their description summary. Our main goal, more
generally, was to determine which features, including (but not limited to)
sentiment and description length, most influence a host's occupancy rate. We
define sentiment score through a natural language algorithm process, based on
the AFINN dictionary. Using AirBnB data on New York City, our hypothesis is
that higher sentiment scores (more positive descriptions) and longer summary
length lead to higher occupancy rates. Our results show that while longer
summary length may positively influence occupancy rates, more positive summary
descriptions have no effect. Instead, we find that other factors such as number
of reviews and number of amenities, in addition to summary length, are better
indicators of occupancy rate.
