Extracting Entities of Interest from Comparative Product Reviews
Jatin Arora Sumit Agrawal Pawan Goyal Sayan Pathak
This paper presents a deep learning based approach to extract product
comparison information out of user reviews on various e-commerce websites. Any
comparative product review has three major entities of information: the names
of the products being compared, the user opinion (predicate) and the feature or
aspect under comparison. All these informing entities are dependent on each
other and bound by the rules of the language, in the review. We observe that
their inter-dependencies can be captured well using LSTMs. We evaluate our
system on existing manually labeled datasets and observe out-performance over
the existing Semantic Role Labeling (SRL) framework popular for this task.