中北大学学报（自然科学版）2024，Vol.45Issue(1)：105-113,9.DOI:10.3969/j.issn.1673-3193.2024.01.014
基于多任务学习的同行评审细粒度情感分析模型
Fine-Grained Sentiment Analysis Model of Peer Review Based on Multi-Task Learning
朱金秋 1檀健 1韩斌彬 1殷秀秀1
作者信息
- 1. 南京邮电大学 理学院,江苏 南京 210023
- 折叠
摘要
Abstract
The peer review of academic papers can directly reflect the subjective evaluation of reviewers on the papers,and the extraction of sentiment information from the review text is beneficial to mining rich information of reviewers'evaluation on each dimension of the papers.The existing sentiment analysis task could only extract the single review dimension and sentiment of experts.A fine-grained sentiment analysis model for peer review is proposed based on multi-task learning.The model is equipped with the ability to accomplish both attribute word extraction and fine-grained sentiment analysis tasks by adding the BiLSTM-CRF module to the BERT-LCF model in a multi-task learning framework.Compared with the traditional single-task fine-grained sentiment analysis model based on the Pipeline model,the proposed model can complete the review attribute extraction and sentiment analysis tasks simultaneously while ensur-ing the accuracy of the model.In the two tasks,F1-score of the proposed model reaches 89.01%and 90.71%,respectively.In addition,the introduction of BiLSTM-CRF module has a certain enhancement effect on the review text attribute word extraction task in a multi-task scenario,as demonstrated by com-parison experiments.关键词
同行评审/多任务学习/属性词抽取/细粒度情感分析/BiLSTM-CRFKey words
peer review/multi-task learning/attribute word extraction/fine-grained sentiment analysis/BiLSTM-CRF分类
信息技术与安全科学引用本文复制引用
朱金秋,檀健,韩斌彬,殷秀秀..基于多任务学习的同行评审细粒度情感分析模型[J].中北大学学报（自然科学版）,2024,45(1):105-113,9.
