# Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box Office
## 3 Framework of the Microblog-Based Short-Term Sales Prediction
### Social Influence
Although these two prediction models still have some errors in the prediction of film box office, we can see that both can provide a precise short-term prediction for the box office. In real applications, enterprises can make decisions based on the predicted trend, which can benefit the short-term marketing strategy of enterprises. Figure 3: Prediction results of the SVR model with different features. Figure 2: Prediction results of the LR model with different features. ## 5 Conclusions and Future Work

With the explosive development of microblogs, predicting future information based on microblogs is a recent research focus. In this paper, considering the timely updates and interaction of microblogs, we presented a new framework to use microblogs to predict short-term product sales. The novel idea of the proposed framework is to integrate the sentiment polarity and the microblog influence into a prediction model, such as the LR model and the SVR model. We conducted experiments on a real dataset concerning the early box office prediction, which was crawled from Sina Weibo, the biggest microblogging platform in China. The results demonstrated that the proposed sentiment polarity 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. This study only presents an initial step towards short-term product sales prediction, and several limitations exist. First, real applications call for real-time processing and predicting frameworks, which are not tackled in this paper. Real-time processing of microblogs needs to devise some efficient stream-processing methods. Second, this paper only conducted a case study focusing on two small datasets due to the lack of public datasets. In the future, we will investigate several issues. First, we will collect data from other fields and conduct experimental verification, such as real estate prices and car sales. Second, we will consider other prediction models, e.g., deep learning models [31; 32], in our future work. Third, the sentiment polarity detection in this paper adopted a straightforward method. We will study more effective approaches, such as multi-dimensional sentiment analysis [33] and domain-specific sentiment analysis [34], to enhance the effectiveness of the sentiment analysis. Finally, we did not measure the credibility of microblog messages [35; 36]. However, this issue has been recognized as a challenge for years. In the future, we will adopt existing approaches in information credibility evaluation to enhance the precision of the sales prediction. J.Z., conceptualization, funding acquisition, project administration, supervision, and writing--review and editing; F.X., data curation, methodology, validation, and writing--original draft preparation; P.J., project administration, supervision, and writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research was funded by the Anhui Philosophy and Social Science Foundation (grant number AHSKY2021D15) and the Humanities and Social Sciences Research Project of the Anhui Provincial Department of Education (grant number SK2020A0036). **Institutional Review Board Statement:** Not applicable. Figure 4: Comparison of the prediction models with real sales. ## References

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