Deep Analysis of Visual Product Reviews

TL;DR

This paper introduces a hierarchical deep learning architecture to analyze visual product reviews, focusing on classifying product categories and predicting review scores from images, addressing the lack of linguistic feedback.

Contribution

It presents a novel hierarchical model for visual review analysis and creates a new database of real visual reviews for training and evaluation.

Findings

Achieved 57.48% performance improvement over single-level models

Developed a new database of real visual product reviews

Demonstrated promising results in product categorization and score prediction

Abstract

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.