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

Author

Listed:
  • Chuanmin Mi

    (Nanjing University of Aeronautics and Astronautics)

  • Mingzhu Li

    (Nanjing University of Aeronautics and Astronautics)

  • Annisa Fitria Wulandari

    (Nanjing University of Aeronautics and Astronautics)

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.

Suggested Citation

  • Chuanmin Mi & Mingzhu Li & Annisa Fitria Wulandari, 2024. "Predicting video views of web series based on comment sentiment analysis and improved stacking ensemble model," Electronic Commerce Research, Springer, vol. 24(4), pages 2637-2664, December.
    • Handle: RePEc:spr:elcore:v:24:y:2024:i:4:d:10.1007_s10660-022-09642-9
    DOI: 10.1007/s10660-022-09642-9
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