Title of Paper:Predicting video views of web series based on comment sentiment analysis and improved stacking ensemble model

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Affiliation of Author(s):Nanjing University of Aeronautics and Astronautics, College of Economics and Management

Teaching and Research Group:管理科学与工程

Journal:Electronic Commerce Research

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

Abstract: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.

Discipline:Management Science

Document Type:J

Volume:24

Issue:12

Page Number:2637-2644

Translation or Not:no

Date of Publication:2024-12-10

Included Journals:SSCI

Co-author:Annisa Fitria Wulandari

Correspondence Author:Mingzhu Li