基于概念格融合模型的垃圾评论识别研究  

Study on Spam Comment Recognition Based on Conceptual Lattice Fusion Modeling

作　　者：刘伟江[1] 马小雯 王博[2] Liu Weijiang;Ma Xiaowen;Wang Bo(School of Business and Management,Jilin University,Changchun 130012,China;Aesthetic Education Center,Beihang University,Beijing 100091,China)

机构地区：[1]吉林大学商学与管理学院,吉林长春130012 [2]北京航空航天大学美育中心,北京100091

出　　处：《现代情报》2025年第4期23-35,共13页Journal of Modern Information

基　　金：国家社会科学基金重大项目“大数据方法在宏观经济预测中的应用研究”(项目编号:23&ZD075)。

摘　　要：[目的/意义]为有效解决基元学习器和集成模型对单形态特定模式的依赖和局限,本文通过加大观察粒度将分类器拓展为可适应多形态混合模式的分类器,以期提升模型理解能力和分类能力。[方法/过程]本文以概念集替代原始特征,引入互斥概念集和正交样本集的概念,对样本进行分离、归纳和融合,构建概念格融合模型,并从模型特质、模型能力、模型品质及过拟合4个方面对模型进行评价。[结果/结论]以亚马逊23971条评论为样本集的测算结果表明,概念格融合模型在准确性、稳定性、抗干扰性等方面都有较大提升,且模型评价结果表明该模型具有更佳的内在品质。[Purpose/Significance]In order to effectively solve the dependence and limitation of primitive learners and integrated models on monomorphic specific patterns,this paper expands the classifier into one that can adapt to polymorphic mixed patterns by increasing the observation granularity,with a view to enhancing the model comprehension and classification ability.[Method/Process]In this paper,the study replaced the original features with concept sets,introduced the concepts of mutually exclusive concept sets and orthogonal sample sets,separated,generalized and fused the samples,constructed a concept lattice fusion model and evaluated the model in four aspects:model traits,model capability,model quality and overfitting.[Result/Conclusion]The measurement results with a sample set of 23971 Amazon reviews show that the concept lattice fusion model has a greater improvement in accuracy,stability,and anti-interference,and the model evaluation results indicate that the model has better intrinsic qualities.

关 键 词：垃圾评论 基元学习器 集成模型 概念格 概念格融合模型 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]