Using consumption emotional features to predict web-show viewership

Today an increasing number of TV shows and movies are released on online video streaming platforms. This study proposes a forecasting modeling framework that uses measures of a show’s consumption emotional features, or viewer sentiments triggered by the show’s production emotional features such as plot, as predictors to forecast a web show’s viewership. Our forecasting modeling framework has three components: feature construction, feature selection through in-sample prediction, and out-of-sample forecasting. In feature construction, we take advantage of the increasingly popular live commenting function in video streaming, which allows viewers to post spontaneous, visceral comments while watching. We utilize machine learning techniques to process the voluminous, unstructured live comment data to form “emotion waves,” which depict the evolution in viewers’ moment-to-moment sentiments throughout the show. We characterize emotion waves to form measures of consumption emotional features. We separately characterize positive and negative emotion waves, as well as their relative positions, and also separately characterize emotion waves in different narrative segments of a show. In feature selection, we use an in-sample prediction model to verify our proposed measures and use only key measures with significant impacts to build the forecasting model. Lastly, in out-of-sample forecasting, we show that a small number of key measures formed over a small sample of live comments available shortly after a show’s release can effectively forecast the show’s viewership accumulated in an extended period after its release.