融合LGBM和SHAP的企业负面事件下微博用户极端情感可解释模型  

An Explanatory Model for Weibo Users'Extreme Sentiment Under Negative Corporate Events with LGBM and SHAP

作　　者：蒋建洪[1] 李梦欣 Jiang Jianhong;Li Mengxin(Commercial College,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学商学院,桂林541004

出　　处：《数据分析与知识发现》2024年第7期103-117,共15页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:71940008)的研究成果之一。

摘　　要：【目的】提取用户外部刺激和认知评价指标,构建企业负面事件下微博用户极端情感影响因素模型,利用SHAP解释各特征变量的影响。【方法】基于认知情感理论、社会影响理论、情感评价模型、LDA模型、扎根理论确定外部刺激和认知评价指标,并将两类指标包含的特征变量作为输入,极端情感变量作为输出,构建极端情感影响因素模型。通过4个模型性能对比,将最优模型与SHAP模型融合进行可视化展示。【结果】认知评价维度提取出7个特征变量;LGBM模型的准确率、精确率、F1值分别达到0.88、0.90、0.93,优于其他对比模型;从特征变量对微博用户极端情感产生的影响程度来看,认知评价维度普遍高于外部刺激维度,且各特征变量的影响方式有所不同。【局限】需要探索更多影响因素及更广泛的企业负面事件类型,算法性能有待提高。【结论】本研究提出的模型优化了扎根编码过程,可视化各特征变量对极端情感的影响程度、影响方向、影响大小及影响方式,可以为企业解决网络口碑负面化的问题提供理论依据。[Objective]This paper extracts users'external stimuli and cognitive evaluation indicators to construct a model of influencing factors for Weibo users'extreme sentiment under corporate negative events.We utilized the SHAP model to explain the impact of each feature variable.[Methods]Based on cognitive-affective theory,social influence theory,emotional appraisal model,LDA model,and grounded theory,this study determined the external stimuli and cognitive evaluation indicators.We used the features contained in these two types of indicators as inputs and extreme emotion variables as outputs to construct the model of influencing factors.By comparing the performance of the four models,the optimal model is integrated with the SHAP model for visual display.[Results]We extracted seven feature variables from the cognitive evaluation dimension.The LGBM model achieved an accuracy,precision,and F1 score of 0.88,0.90,and 0.93,respectively,outperforming other comparative models.Regarding the impact of feature variables on the extreme emotions of Weibo users,the cognitive evaluation dimension generally had a higher influence than the external stimulus dimension,and the impact of each feature variable varied.[Limitations]We should explore more influencing factors and a wider range of corporate negative event types.The algorithm's performance needs to be improved.[Conclusions]The proposed model optimizes the grounded coding process and visualizes each feature variable's influence degree,direction,magnitude,and manner on extreme emotions.This study provides a theoretical basis for enterprises to address negative online reputation issues.

关 键 词：企业负面事件 LGBM SHAP 扎根理论 认知情感理论 

分 类 号：G206[文化科学—传播学]