Sentiment Analysis of Peer Review Texts Based on Pu-Learning

被引：1

作者：

Lin, Yuan ^{[1
]}

Wang, Kaiqiao ^{[2
]}

Yang, Liang ^{[3
]}

Lin, Hongfei ^{[3
]}

Ren, Lu ^{[3
]}

Ding, Kun ^{[1
]}

机构：

[1] Institute of Science of Science and Science & Technology, Dalian University of Technology, Liaoning, Dalian

[2] Haikou Laboratory, Institute of Acoustic, Chinese Academy of Sciences, Haikou

[3] Information Retrieval Laboratory, Dalian University of Technology, Liaoning, Dalian

来源：

Computer Engineering and Applications | 2024年 / 59卷 / 03期

关键词：

data mining; peer review; pu-learning; sentiment analysis;

D O I：

10.3778/j.issn.1002-8331.2108-0341

中图分类号：

学科分类号：

摘要：

There have been some researches on the sentiment analysis of peer review text, including the task of predicting the overall recommendation through a peer review text written by reviewer for a submission. Existing works integrate the embedding of the paper or abstract, utilizing neural network to learn the high-level representation of paper or abstract and review text to predict reviewer’s overall recommendation, which make the algorithm very complicated but the effect is not substantially improved. To solve this issue, a mechanism called OSA(opinionated sentence attention)is proposed to make opinionated sentences get more attention in sentiment analysis model. Specifically, this paper employs a positive-unlabeled learning method to learn opinionated sentence features form Top-N sentences of peer review texts so that every sentence of all review texts gets a opinionated weight, then these weights are dotted with penultimate layer of neural network to get the final prediction. OSA is evaluated with different neural networks on ICLR 2017—2018 datasets, experimental results verify that OSA is of high efficiency and achieves outstanding performance on two datasets. © 2024 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.

引用

页码：143 / 149

页数：6

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