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计算机系统应用英文版:2023,32(6):1-11
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基于局部上下文关键词的方面级情感分析
(华南师范大学 软件学院, 佛山 528225)
Aspect Level Sentiment Analysis Based on Local Context Keyword
(School of Software, South China Normal University, Foshan 528225, China)
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Received:November 22, 2022    Revised:December 23, 2022
中文摘要: 针对目前大多数方面级情感分析方法存在着没有重点关注局部上下文中关键词特征的问题. 本文提出了一种基于局部上下文关键词特征提取及增强的方面级情感分析模型LCPM (local context pos mask). 首先提出了局部上下文词性掩码机制, 提取方面词周围重要词的特征, 减少噪声词的干扰. 其次对损失函数进行修改, 让模型重点关注与方面词有关的局部上下文关键词特征, 提升模型情感分类的表现. 最后设计了一种门控机制, 模型可以动态学习权重系数, 给局部上下文关键词特征和全局上下文特征分配不同的权重系数. 在4个公开数据集上的实验结果表明, 与现有的方面级情感分析模型相比, 准确率和MF1值都有提高, 验证了局部上下文关键词提取及增强的有效性, 在方面级情感分析任务上有较大的应用价值.
Abstract:Most aspect level sentiment analysis methods do not focus on keyword features in the local context. Therefore, this study proposes an aspect level sentiment analysis model LCPM (local context pos mask) based on local context keyword feature extraction and enhancement. First, a local context part of the speech mask mechanism is proposed to extract the important words features around aspect words and reduce the interference of noise words. Second, the loss function is modified, so that the model focuses on the local context keyword features related to aspect words and improves the performance of the model’s sentimental classification. Finally, a gating mechanism is designed. The model can dynamically learn the weight coefficients and assign different weight coefficients to local context keyword features and global context features. The experiments on four open datasets show that, compared with existing aspect level sentiment analysis models, the proposed model has higher accuracy and MF1 value, which verifies the effectiveness of local context keyword extraction and enhancement and is of application significance in aspect level sentiment analysis tasks.
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基金项目:国家自然科学基金(62076103); 广东省普通高校人工智能重点领域专项(2019KZDZX1033); 广东省信息物理融合系统重点实验室(2020B1212060069); 广东省基础与应用基础研究基金(2021A1515011171); 广州市基础研究计划基础与应用基础研究项目(202102080282)
引用文本:
曾碧卿,黄梓芃.基于局部上下文关键词的方面级情感分析.计算机系统应用,2023,32(6):1-11
ZENG Bi-Qing,HUANG Zi-Peng.Aspect Level Sentiment Analysis Based on Local Context Keyword.COMPUTER SYSTEMS APPLICATIONS,2023,32(6):1-11