Deciphering the Reviewer's Aspectual Perspective: A Joint Multitask Framework for Aspect and Sentiment Extraction from Scholarly Peer Reviews

Hardik Arora, Kartik Shinde, Tirthankar Ghosal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Peer reviews are one of the most important artifacts in scholarly communications. Peer reviews can serve as a rich source of knowledge discovery from texts that are human-generated and also opinionated on the paper under scrutiny. Reviewers comment on several implicit aspects of the paper (Originality, Soundness, Clarity, Appropriateness, etc.) where they sometimes appreciate, sometimes discuss, or sometimes question or criticize the work. Hence, correctly understanding the reviewer's aspectual perspective on the paper is crucial for chairs/editors to take a stand and also for the authors to respond or revise accordingly. In this paper, we introduce MASEPR, a novel multitask deep neural architecture to jointly discover the aspects and associated sentiments from the peer review texts. Our proposed approach leverages the knowledge sharing between aspect and sentiment lexicons to generate predictions. We outperform the standard baselines by a significant margin. We also make our codes available at https://github.com/cruxieu17/MASEPR.

Original languageEnglish
Title of host publicationProceedings - 2023 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages35-46
Number of pages12
ISBN (Electronic)9798350399318
DOIs
StatePublished - 2023
Event2023 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2023 - Santa Fe, United States
Duration: Jun 26 2023Jun 30 2023

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
Volume2023-June
ISSN (Print)1552-5996

Conference

Conference2023 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2023
Country/TerritoryUnited States
CitySanta Fe
Period06/26/2306/30/23

Keywords

  • Aspect-based Sentiment Analysis
  • Deep Neural Network
  • Peer Reviews
  • SHAP (SHapley Additive exPlanations)

Fingerprint

Dive into the research topics of 'Deciphering the Reviewer's Aspectual Perspective: A Joint Multitask Framework for Aspect and Sentiment Extraction from Scholarly Peer Reviews'. Together they form a unique fingerprint.

Cite this