# Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box Office
## 1 Introduction  
Microblogs have emerged as primary social-network media for years due to their special properties, including convenient information release, real-time updates, rapid dissemination, and free interaction [1, 2]. Microblogs can deliver timely information compared with other media-like web pages and blogs. With the increasing amount of microblog data, utilizing microblog data in decision-making tasks is a critical issue that receives attention from both academia and industries [3].  
In particular, the rapid spread of microblogs on social networks can quickly form a hot spot of public opinion. Therefore, by evaluating the influence of microblogs, we can quickly find the news sources that spread fast on social networks and take corresponding actions, e.g., preventing the spread of illegal news and rumors, which is of great significance for public opinion analysis and emergency management [4]. Furthermore, the measurement of microblog influence can also assist the evaluation of microblog users' influence on social networks. Therefore, accurate evaluation of microblog influence is also of great value for business decision-making [5].  
On the other hand, the sentiment polarity of microblogs can often accurately reflect users' actual views. Because of the huge number of microblogging users, the opinion of microblogging users towards a specific event can reflect the social sentiment polarity of the event [6], which can be utilized to analyze and predict users' behavior in social life.  
Furthermore, microblog data can be obtained freely on the Internet, offering a more economical way for enterprises to conduct data-driven analysis and prediction than traditional investigation-based approaches.  
Due to the lack of large-scale datasets for product sales prediction, this paper studies the feasibility of predicting short-term product sales with the support of microblogs based on a case-study-based perspective. We first develop a framework to enhance the prediction of short-term sales. Then, we take movie box office prediction as a case study to demonstrate the effectiveness of our proposal. Briefly, we make the following contributions in this paper:  
1. Unlike previous works that focused on event detection or prediction from microblogging platforms, this paper concentrates on short-term product sales based on microblogs and presents a new framework based on sentiment analysis and social influence.
2. We propose a new feature called social influence to reflect the impact of social-network information diffusion on short-term product sales. A new algorithm is presented to measure the social influence in the paper.
3. We conduct experiments on a real dataset to evaluate the performance of the proposed framework. We take movie box office prediction as a case study and analyze the prediction performance of two regression models. The results show that the proposed sentiment feature and influence feature of microblogs play a positive role in improving the prediction precision.  
The remainder of the paper is organized as follows. Section 2 summarizes the related work. Section 3 presents the framework of microblog-based sales prediction. Section 4 reports the experimental results, and finally, Section 5 concludes the paper.