An Explanatory Model for Weibo Users'Extreme Sentiment Under Negative Corporate Events with LGBM and SHAP
[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 Fl 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.
Negative Events of EnterprisesLGBMSHAPGrounded TheoryCognitive Emotional Theory
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