Extracting Entities of Interest from Comparative Product Reviews

Jatin Arora, Sumit Agrawal, Pawan Goyal, Sayan Pathak
10/31/2023
cs.IRcs.CLcs.LG

Official code: jatinarora2702/Review-Information-Extraction (6 stars, last commit 2018-09-13).

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.

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Code Implementations(1)

jatinarora2702/Review-Information-ExtractionOfficial100%

Code for paper, 'Extracting Entities of Interest from Comparative Product Reviews', CIKM'17

61PythonSep 9, 20188 years ago

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Cite this paper

@article{arora2023extracting,
  title  = {Extracting Entities of Interest from Comparative Product Reviews},
  author = {Jatin Arora and Sumit Agrawal and Pawan Goyal and Sayan Pathak},
  year   = {2023},
  eprint = {2310.20274},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.20274v1}
}

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