  Within the context of review analytics, aspects are the features of products
and services at which customers target their opinions and sentiments. Aspect
detection helps product owners and service providers to identify shortcomings
and prioritize customers' needs, and hence, maintain revenues and mitigate
customer churn. Existing methods focus on detecting the surface form of an
aspect by training supervised learning methods that fall short when aspects are
latent in reviews. In this paper, we propose an unsupervised method to extract
latent occurrences of aspects. Specifically, we assume that a customer
undergoes a two-stage hypothetical generative process when writing a review:
(1) deciding on an aspect amongst the set of aspects available for the product
or service, and (2) writing the opinion words that are more interrelated to the
chosen aspect from the set of all words available in a language. We employ
latent Dirichlet allocation to learn the latent aspects distributions for
generating the reviews. Experimental results on benchmark datasets show that
our proposed method is able to improve the state of the art when the aspects
are latent with no surface form in reviews.
