# 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
To prove that the introduction of the microblog influence feature can improve prediction accuracy, we take the sentiment polarity feature alone as the comparison basis and observe the prediction effect of the proposed prediction framework on the film box office by comparing the two models. The box office prediction results of different influence features in different prediction models are shown in Table 6.

As shown in Table 6, in terms of prediction accuracy, whether in the LR model or the SVR model, when the influence feature of a microblog is introduced, the prediction effect is improved, with higher accuracy and less error. Moreover, the error of the SVR model is generally smaller than that of the LR model, which shows that compared with the linear relationship, the variables used for prediction are more likely to have a more complex nonlinear relationship. We tested the effect of the microblog influence measurement method on the prediction performance and used the influence based on forwarded comments and the influence based on forwarded comments and user credibility as the comparison method. Based on the influence calculated by these two methods, we used the extraction method of microblog influence characteristics proposed in Section 3.3 to extract the influence feature of microblogs to test the prediction effectiveness. The comparative experiments on the prediction performance of different features show that the SVR model based on the weighted sentiment and influence features achieves the best performance, which achieves the minimum MAPE value on the two models and two datasets, indicating that the influence obtained by our proposed method can improve the accuracy of prediction. Since the information prediction framework proposed in this paper is based on microblogs, we used the data without introducing any microblog characteristics (sentiment polarity characteristics and microblog influence characteristics). That is, only the box office of the previous day is used as the input for predicting the box office. Figures 2 and 3 summarize the average prediction results of the LR and SVR models with different features. Here, we use the first movie _Breakup Buddies_ as a case study. The data before 2014-10-15 is used as the training data. Then, we plot the prediction results of different models in the figures. To visualize the prediction precision clearly, we also show the real sales of the movie in the figures, which are taken from the China film box office data center. We can see that using the sentiment feature and the influence feature is helpful to improve the prediction precision. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Model** & **Features** & _Breakup Buddies_ & _Interstellar_ \\ \hline \multirow{2}{*}{LR} & sentiment (weighted) & 0.471416 & 2.636955 \\ \cline{2-4}  & sentiment (weighted) + influence & 0.365470 & 1.128859 \\ \hline \multirow{2}{*}{SVR} & sentiment (weighted) & 0.347621 & 0.817183 \\ \cline{2-4}  & sentiment (weighted) + influence & 0.335603 & 0.694272 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MAPE with the influence feature. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Model** & **Features** & _Breakup Buddies_ & _Interstellar_ \\ \hline \multirow{2}{*}{LR} & sentiment (weighted) & 0.471416 & 2.636955 \\ \cline{2-4}  & sentiment (not weighted) & 0.543178 & 2.827116 \\ \hline \multirow{2}{*}{SVR} & sentiment (weighted) & 0.347621 & 0.817183 \\ \cline{2-4}  & sentiment (not weighted) & 0.363807 & 0.837459 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MAPE with the sentiment feature. Figure 4 compares the real sales, the LR model with the two features, and the SVR model with the two features. Compared with the LR model, the predicted value of the SVR model is quite close to the real value on some days, especially from 28 October to 31 October, which almost overlaps with the real sales.