Abstract:With the rapid development of network dramas in recent years, the research on broadcast volume has gradually attracted attention. Broadcast volume reflects the reputation and popularity of a network drama, which are closely related to the profits of producers and investors. However, current research rarely considers the impact of the sentiments in viewers’ comments on broadcast volume, and the forecasting models are simple. Consequently, the accuracy of prediction needs to be further improved. After a sentiment analysis of users’ comments, we construct a stacking ensemble learning model to predict the broadcast volume of network dramas in China. Using the SO-PMI (semantic orientation-pointwise mutual information) algorithm, we build a sentiment dictionary in the network drama domain. A basic sentiment dictionary and the number of likes are also taken into account to calculate the comment sentiment scores, which are then added into the prediction index system. With random forest (RF), GBDT (gradient boosting decision tree), XGBoost (extreme gradient boosting), and LightGBM (light gradient boosting machine) as base learners and MLR as a meta learner, a stacking prediction model is constructed to predict the broadcast volume of a network drama in stages. The broadcast volume of the next week can be forecasted with data of the current week. Finally, the results of different models are compared and analyzed, and the importance scores of predictive variables are obtained. The experimental results show that the determination coefficient R-square of the proposed model reaches 0.89, which is higher than that of a single base learner (maximum 0.84) as well as that of the stacking model without sentiment score variables (0.81). It can be concluded that with sentiment score variables, the proposed stacking ensemble learning model delivers better prediction accuracy on the broadcast volume of network dramas than that of traditional models.