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所属单位：南京航空航天大学经济与管理学院

教研室：管理科学与工程

发表刊物：Electronic Commerce Research

关键字：Web series; Video views prediction; SO-PMI algorithm; Comment sentiment analysis; Stacking ensemble model; Precision weighted average

摘要：Web series, which is broadcasted on the network, has been developing rapidly through the advancement of mobile network and electronic commerce. This paper aims to predict video views of web series based on comment sentiment analysis and improved stacking ensemble model. Apart from conventional variables, sentiment score variables calculated from viewer comments were added as input variables. Based on sentiment lexicons built with smooth SO-PMI algorithm, we calculated sentiment scores of comments by assigning weights to modifiers and the number of “likes”. We proposed the improved stacking ensemble model for prediction, which utilizes the precision weighted average method. Random Forest, Gradient Boosting Decision Tree, Extreme Gradient Boosting and Light Gradient Boosting Machine were taken as base learners of the stacking model. The results showed that by adding sentiment score variables, the improved stacking ensemble model can further improve the predictive performances.

论文类型：期刊论文

学科门类：管理学

文献类型：J

卷号：24

期号：12

页面范围：2637-2644

是否译文：否

发表时间：2024-12-10

收录刊物：SSCI

合写作者：Annisa Fitria Wulandari

通讯作者：Mingzhu Li