How are PreLaunch online movie reviews related to box office revenues?

Abstract

This paper studies the dynamic patterns of the prelaunch online movie reviews, or movie electronic word-of-mouth (eWOM), over time and investigates their relations to the subsequent box office revenues. The volume and valence of prelaunch eWOM have been shown to be early indicators of strong or weak box office. The time patterns of prelaunch eWOM evolution, which are essentially functional data, on the other hand, tend to be overlooked. We apply the functional principal component analysis, a dimension reduction technique in functional data analysis, to analyze the dynamic patterns of various quantile trajectories of the movie eWOM, instead of directly studying the whole eWOM functional data. The functional principal component (FPC) scores of quantile trajectories at various quantile levels are used to predict the box office revenues. We use the sparse group lasso method to select the quantile levels and individual FPC scores that make significant contributions to the prediction of box office revenues. The results show that compared with other measures, such as valence and variance, the top-end quantiles would be a better measure in capturing the relations between the prelaunch product ratings time pattern and launch sales.