Extracting Entities of Interest from Comparative Product Reviews

October 31, 2023 · Entered Twilight · 🏛 International Conference on Information and Knowledge Management

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Authors Jatin Arora, Sumit Agrawal, Pawan Goyal, Sayan Pathak arXiv ID 2310.20274 Category cs.IR: Information Retrieval Cross-listed cs.CL, cs.LG Citations 18 Venue International Conference on Information and Knowledge Management Repository https://github.com/jatinarora2702/Review-Information-Extraction ⭐ 6 Last Checked 2 months ago

Abstract

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.