  With the proliferation of the e-commerce industry, analyzing customer
feedback is becoming indispensable to a service provider. In recent days, it
can be noticed that customers upload the purchased product images with their
review scores. In this paper, we undertake the task of analyzing such visual
reviews, which is very new of its kind. In the past, the researchers worked on
analyzing language feedback, but here we do not take any assistance from
linguistic reviews that may be absent, since a recent trend can be observed
where customers prefer to quickly upload the visual feedback instead of typing
language feedback. We propose a hierarchical architecture, where the
higher-level model engages in product categorization, and the lower-level model
pays attention to predicting the review score from a customer-provided product
image. We generated a database by procuring real visual product reviews, which
was quite challenging. Our architecture obtained some promising results by
performing extensive experiments on the employed database. The proposed
hierarchical architecture attained a 57.48% performance improvement over the
single-level best comparable architecture.
