融合边缘采样和Tri-training的用户评论情感分析方法  

Sentiment Analysis of User Reviews Integrating Margin Sampling and Tri-training

作　　者：江亿平 张婷[1] 夏争鸣 李玉花 张兆同[1] Jiang Yiping;Zhang Ting;Xia Zhengming;Li Yuhua;Zhang Zhaotong(College of Information Management,Nanjing Agricultural University,Nanjing 210031,China;College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210031,China)

机构地区：[1]南京农业大学信息管理学院,南京210031 [2]南京农业大学人工智能学院,南京210031

出　　处：《数据分析与知识发现》2024年第5期102-112,共11页Data Analysis and Knowledge Discovery

基　　金：江苏省社会科学基金资助项目(项目编号:21GLC003);教育部人文社会科学研究规划基金项目(项目编号:22YJA630033);江苏省研究生科研与实践创新计划项目(项目编号:SJCX23_0229)的研究成果之一。

摘　　要：【目的】针对用户评论数据量大、情感倾向模糊、内容短小等特点,提出融合边缘采样和Tri-training的用户评论情感分析方法。【方法】通过构建基于一对多拆解策略的多分类支持向量机,并融合考虑余弦相似度的边缘采样策略构造初始集,提出结合软投票机制的Tri-training算法。【结果】本文算法对Tri-training算法投票机制的改进,进一步减小了多个分类器对于样本分类投票判断失误的概率,使所有类别精确率均在79%以上。【局限】未考虑多媒体数据的信息提取。【结论】与传统及近年改进的半监督学习算法相比,本文提出的融合边缘采样和Tri-training的算法在分类准确率和效率上具有一定的优越性。[Objective]This paper proposes a sentiment analysis method for user reviews integrating margin sampling and tri-training.It addresses the issues of the large volume of user reviews,ambiguous sentiment tendencies,and short content.[Methods]First,we constructed a multi-class support vector machine based on a one-vs-all decomposition strategy.Then,we integrated a margin sampling strategy considering cosine similarity to create an initial set.Finally,we proposed a Tri-training algorithm combining a soft voting mechanism.[Results]The proposed algorithm improved the voting mechanism in the Tri-training algorithm,which further reduced the probability of misjudgment in sample classification by multiple classifiers.All categories achieved precision rates above 79%.[Limitations]The proposed method does not consider extracting information from multimedia data.[Conclusions]Compared with traditional and recently improved semi-supervised learning algorithms,the proposed algorithm demonstrates classification accuracy and efficiency superiority.

关 键 词：用户评论 情感分析 边缘采样 TRI-TRAINING 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] G350[自动化与计算机技术—计算机科学与技术]