文章基本信息

标题：Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box Office
本地全文：下载
作者：Jie Zhao ; Fangwei Xiong ; Peiquan Jin 等
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2022
卷号：14
期号：5
页码：141
DOI：10.3390/fi14050141
语种：English
出版社：MDPI Publishing
摘要：Microblogs are one of the major social networks in people’s daily life. The increasing amount of timely microblog data brings new opportunities for enterprises to predict short-term product sales based on microblogs because the daily microblogs posted by various users can express people’s sentiments on specific products, such as movies and books. Additionally, the social influence of microblogging platforms enables the rapid spread of product information, implemented by users’ forwarding and commenting behavior. To verify the usefulness of microblogs in enhancing the prediction of short-term product sales, in this paper, we first present a new framework that adopts the sentiment and influence features of microblogs. Then, we describe the detailed feature computation methods for sentiment polarity detection and influence measurement. We also implement the Linear Regression (LR) model and the Support Vector Regression (SVR) model, selected as the representatives of linear and nonlinear regression models, to predict short-term product sales. Finally, we take movie box office predictions as an example and conduct experiments to evaluate the performance of the proposed features and models. The results show that the proposed sentiment feature and influence feature of microblogs play a positive role in improving the prediction precision. In addition, both the LR model and the SVR model can lower the MAPE metric of the prediction effectively.