  Online review platforms are a popular way for users to post reviews by
expressing their opinions towards a product or service, as well as they are
valuable for other users and companies to find out the overall opinions of
customers. These reviews tend to be accompanied by a rating, where the star
rating has become the most common approach for users to give their feedback in
a quantitative way, generally as a likert scale of 1-5 stars. In other social
media platforms like Facebook or Twitter, an automated review rating prediction
system can be useful to determine the rating that a user would have given to
the product or service. Existing work on review rating prediction focuses on
specific domains, such as restaurants or hotels. This, however, ignores the
fact that some review domains which are less frequently rated, such as
dentists, lack sufficient data to build a reliable prediction model. In this
paper, we experiment on 12 datasets pertaining to 12 different review domains
of varying level of popularity to assess the performance of predictions across
different domains. We introduce a model that leverages aspect phrase embeddings
extracted from the reviews, which enables the development of both in-domain and
cross-domain review rating prediction systems. Our experiments show that both
of our review rating prediction systems outperform all other baselines. The
cross-domain review rating prediction system is particularly significant for
the least popular review domains, where leveraging training data from other
domains leads to remarkable improvements in performance. The in-domain review
rating prediction system is instead more suitable for popular review domains,
provided that a model built from training data pertaining to the target domain
is more suitable when this data is abundant.
