  Airbnb is one of the most successful examples of sharing economy
marketplaces. With rapid and global market penetration, understanding its
attractiveness and evolving growth opportunities is key to plan business
decision making. There is an ongoing debate, for example, about whether Airbnb
is a hospitality service that fosters social exchanges between hosts and
guests, as the sharing economy manifesto originally stated, or whether it is
(or is evolving into being) a purely business transaction platform, the way
hotels have traditionally operated. To answer these questions, we propose a
novel market analysis approach that exploits customers' reviews. Key to the
approach is a method that combines thematic analysis and machine learning to
inductively develop a custom dictionary for guests' reviews. Based on this
dictionary, we then use quantitative linguistic analysis on a corpus of 3.2
million reviews collected in 6 different cities, and illustrate how to answer a
variety of market research questions, at fine levels of temporal, thematic,
user and spatial granularity, such as (i) how the business vs social dichotomy
is evolving over the years, (ii) what exact words within such top-level
categories are evolving, (iii) whether such trends vary across different user
segments and (iv) in different neighbourhoods.
