融合多语言知识的慕课评论隐式方面情感分析  

Integrating Multilingual Knowledge for Implicit Aspect-Based Sentiment Analysis of MOOC Reviews

作　　者：陈怀博 张会兵[1] 首照宇[1] 潘芳 CHEN Huaibo;ZHANG Huibing;SHOU Zhaoyu;PAN Fang(Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China;Teachers College for Vocational and Technical Education,Guangxi Normal University,Guilin,Guangxi 541004,China)

机构地区：[1]桂林电子科技大学广西可信软件重点实验室,广西桂林541004 [2]广西师范大学职业技术师范学院,广西桂林541004

出　　处：《计算机工程与应用》2025年第5期104-112,共9页Computer Engineering and Applications

基　　金：国家自然科学基金(62267003,62177012);广西可信软件重点实验室研究课题(KX202319);广西人文社会科学发展研究中心资助项目(ZXZJ202203)。

摘　　要：慕课完成率不高的问题严重制约着其高质量发展,慕课评论中隐喻、客观事实描述、讽刺、反问等表达中蕴含的隐式情感更为真实地表达了用户的学习体验,对信息进行分析、利用,从而挖掘出学生关于课程的反馈信息,并做出相应的改善,有助于提升学生满意度以提高慕课完成率。为此,提出一种融合多语言知识的慕课隐式方面情感分析模型来获得更为精准的隐式情感信息。针对前两种表达中缺乏明显情感倾向的特点,引入多重图神经网络来融合词性、语义、句法和义原等多语言知识,充分利用其中的关联关系来挖掘评论中隐含的情感信息。同时,对于后两种表达方式中的情感词与文本真实情感极性不符的问题,构建多层级注意力机制来获取整体语义粗粒度、方面词细粒度中的情感信息。在构建的MOOC数据集上测试模型,准确率和F1指数分别达到90.2%和93.8%,同时在SMP2019-ECISA数据集上的对比实验表明,所提模型的准确率与KC-ISA-BERT等模型相比提升了1.7个百分点。The issue of low completion rates in MOOCs severely restricts their high-quality development.The implicit emotions contained in expressions such as metaphors,objective factual descriptions,sarcasm,and rhetorical questions in MOOC comments more genuinely reflect users’learning experiences.Analyzing and utilizing this information to uncover student feedback on the courses and make corresponding improvements can help enhance MOOC completion rates.To this end,the paper proposes a MOOC implicit aspect sentiment analysis model integrating multilingual knowledge to obtain more accurate implicit sentiment information.To address the lack of clear emotional tendencies in the first two expressions,a multi-graph neural network is introduced to combine multi-language knowledge such as part of speech,semantics,syntax,and semantic primitives,fully utilizing the associated relationships to uncover implicit emotional information in comments.Meanwhile,to address the issue of emotional words not matching the true sentiment polarity in the last two expression methods,a multi-level attention mechanism is constructed to capture emotional information at both the coarse-grained level of overall semantics and the fine-grained level of aspect words.Testing the model on the MOOC dataset for paper construction achieves accuracy and F1 scores of 90.2%and 93.8%,respectively.Comparative experiments on SMP2019-ECISA dataset reveals an improved accuracy of the proposed model by 1.7 percentage points compared to models like KC-ISA-BERT.

关 键 词：隐式情感分析 方面情感分析 图神经网络 多级注意力机制 慕课 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]