报告题目：How Are Pre-Launch Online Movie Reviews Related to Box Office Revenues?
报告人：曹际国教授
时间：2026年3月27日15:00-16:00
地点：创新港涵英楼经济金融研究院8004室榆林研究院
报告人简介：
曹际国博士,加拿大温哥华西蒙弗雷泽大学(Simon Fraser University)统计与精算系教授，加拿大数据科学国家特聘教授(Canada Research Chair in Data Science)，现担任Statistics in Medicine、 Journal of the Royal Statistical Society: Series A等国际优秀统计期刊副主编。曹际国2006年获得加拿大麦吉尔大学(McGill University)博士，2007年美国耶鲁大学博士后出站，长期从事人工智能、机器学习、函数型数据分析(functional data analysis)和估计微分方程的研究。曹际国2021年获得加拿大统计协会(Statistical Society of Canada)和国家数学研究中心(Centre de recherches mathématiques)联合评比的最高奖之一：加拿大国家杰出青年统计学家奖(CRM-SSC award)。曹际国近些年来在国际优秀统计期刊中发表超过100篇文章。
摘要：
This talk focuses on the dynamic patterns of the pre-launch online movie reviews, or movie electronic word-of-mouth (eWOM), over time and their relations to the subsequent box office revenues. The volume and valence of pre-launch eWOM have been shown to be early indicators of strong or weak box office. The time patterns of pre-launch 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 pre-launch product ratings time pattern and launch sales. The data and problem are unique and interesting. We welcome any further research in this topic.
