# 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
Therefore, this paper selects the linear regression model as the benchmark for comparison. In this paper, the LR model as a prediction model is represented as: \(y_{t+1}=\alpha x_{t,1}+\beta x_{t,2}+\gamma y_{t}+\epsilon\), where \(\alpha\), \(\beta\) and \(\gamma\) and the error \(\epsilon\) are obtained from the training using the normal equation method. (2) Support Vector Regression (SVR) [28]. In real life, the relationship between variables as a prediction may be very complex and not necessarily limited to a linear relationship. Therefore, some nonlinear models need to be used for prediction. Therefore, this paper selects the support vector regression model SVR (support vector regression) to describe the relationship between input and output. The SVR model is derived from the support vector machine (SVM) [29]. SVM is a binary classification model that aims to find a hyperplane to distinguish different categories. It has shown better performance in machine learning algorithms than many existing methods. In this paper, the SVR model as a prediction model can be described by: \(y_{t+1}=w^{T}x+b\). Here, \(w=(w_{1},w_{2},w_{3})^{T}\), \(x=(x_{t,1},x_{t,2},y_{t})^{T}\). \(w^{T}\) is the normal vector determining the direction of the hyperplane. \(b\) is the offset, which determines the distance between the hyperplane and the origin point. Note that there are also some other prediction models [30; 31] that can be used in product sales prediction. In general, the LR model and the SVR model can be regarded as the two basic models. As we aim to demonstrate the feasibility of the proposed prediction framework based on sentiment polarity and social influence, we do not include more models in this study and save this issue as one to address in our future works. ## 4 Experiments

### Settings

This paper selects two films as the experimental data, namely the domestic romantic comedy _Breakup Buddies_ and the American science fiction film _Interstellar_. The screenings of the two films are shown in Table 2 and were screened for 34 days and 31 days, respectively. The dataset of this paper is the original microblog data within the corresponding performing period and the forwarding and comment data of these microblogs, which are crawled on Sina Weibo with the keywords of \"Breakup Buddies\" and \"Interstellar,\" respectively. The box office statistics of films come from the daily box office situation released by the official WeChat \"China box office\" of the China film box office data center ([[http://www.cbooo.cn](http://www.cbooo.cn)]([http://www.cbooo.cn](http://www.cbooo.cn)), accessed on 12 February 2022). The daily box office records are generally counted at about 16:00 the next day. After data extraction, the microblog data of the two films are shown in Table 3. The _Breakup Buddies_ forwarding rate was only 7.46%, 2273 of 30,484 microblogs were forwarded, and the comment rate was 30.82%. Although the film performed well in the comedy genre, its plot expression was not recognized by the audience. With the participation of comedians, it did not have a box office of CNY 1.267 billion in 2012, but it achieved a good overall result of CNY 1.169 billion. The forwarding rates and comment rates of _Interstellar_ are slightly higher than those of _Breakup Buddies_, with 11.23% and 37.61%, respectively. The film also achieved a box office result of CNY 748 million in China. The user statistics of the two films are shown in Table 4. Since the maximum number of followers of Sina Weibo is 3000 (Sina Weibo restricts users' behavior of increasing the number of fans by paying attention to others), only the number of users with the maximum number of followers are counted. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Movie ID** & **Movie Title** & **Start Time** & **End Time** & **Days on Show** \\ \hline \#1 & _Breakup Buddies_ & 2014-09-30 & 2014-11-02 & 34 \\ \hline \#2 & _Interstellar_ & 2014-11-12 & 2014-12-12 & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Movie information.