TY - GEN
T1 - Deciphering the Reviewer's Aspectual Perspective
T2 - 2023 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2023
AU - Arora, Hardik
AU - Shinde, Kartik
AU - Ghosal, Tirthankar
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Aspect-based Sentiment Analysis
KW - Deep Neural Network
KW - Peer Reviews
KW - SHAP (SHapley Additive exPlanations)
UR - http://www.scopus.com/inward/record.url?scp=85174582961&partnerID=8YFLogxK
U2 - 10.1109/JCDL57899.2023.00015
DO - 10.1109/JCDL57899.2023.00015
M3 - Conference contribution
AN - SCOPUS:85174582961
T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
SP - 35
EP - 46
BT - Proceedings - 2023 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 June 2023 through 30 June 2023
ER -