In the last decade, online customer reviews increasingly exert influence on
consumers' decision when booking accommodation online. The renewal importance
to the concept of word-of mouth is reflected in the growing interests in
investigating consumers' experience by analyzing their online reviews through
the process of text mining and sentiment analysis. A clustering approach is
developed for Boston Airbnb reviews submitted in the English language and
collected from 2009 to 2016. This approach is based on a mixture of latent
variable models, which provides an appealing framework for handling clustered
binary data. We address here the problem of discovering meaningful segments of
consumers that are coherent from both the underlying topics and the sentiment
behind the reviews. A penalized mixture of latent traits approach is developed
to reduce the number of parameters and identify variables that are not
informative for clustering. The introduction of component-specific rate
parameters avoids the over-penalization that can occur when inferring a shared
rate parameter on clustered data. We divided the guests into four groups --
property driven guests, host driven guests, guests with recent overall negative
stay and guests with some negative experiences.