# 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
\begin{table}
\begin{tabular}{c c c} \hline \hline  & _Breakup Buddies_ & _Interstellar_ \\ \hline \#Original microblogs & 30,484 & 30,459 \\ \hline \#Likes & 57,323 & 73,453 \\ \hline \#Comments & 59,956 & 78,167 \\ \hline \#Forwards & 48,483 & 91,039 \\ \hline \#Micorblogs liked & 10,540 & 12,159 \\ \hline \#Microblogs commented & 9395 & 11,456 \\ \hline \#Microblogs forwarded & 2273 & 3420 \\ \hline \#Most likes & 6142 & 6336 \\ \hline \#Most comments & 3411 & 3782 \\ \hline \#Most forwards & 16,264 & 17,282 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the microblogs about the movies. The sentiment dictionary used in the experiment was independently sorted and labeled by the Information Retrieval Research Office of the Dalian University of Technology. The labeling of emotional words is graded. This paper does not consider the intensity of sentiment but only the positive and negative polarity. The number of topics selected in the LDA model is 100, the value of \(\alpha\) is 0.5, \(\beta\) is 0.1, and the Gibbs sampling iteration is 2000 times. Both prediction models are implemented in the R language. The LR model uses the default function in the R language, and the SVR model uses the SVM function in the LibSVM package (LibSVM. [[https://www.csie.ntu.edu.tw/~cjlin/libsvm/](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)]([https://www.csie.ntu.edu.tw/~cjlin/libsvm/](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)), accessed on 12 February 2022.). The SVR model selects the linear kernel, and the penalty parameter \(C\) used in the kernel is 10. To measure the prediction effect, we use mean absolute percent error (MAPE) [16], the mean value of the percentage error between the predicted value and the actual value, to compare the prediction effect of different features under different prediction models. The smaller the MAPE, the higher the prediction accuracy, and the better the model's performance. The calculation method of MAPE is shown in Formula (10). Here, \(n\) is the number of prediction rounds, \(y\) is the real value, and \(\hat{y}\) is the predicted value. \[MAPE=\frac{1}{n}\sum\frac{|\hat{y}-y|}{y} \tag{10}\]

### Results

The film screening days are generally about 30 days. Therefore, for the datasets of the two films, this paper uses the data of two weeks or 14 days after screening to train a prediction model to predict the box office of the remaining screening days. We first explore the impact of emotional propensity on the prediction effect. Then, the feature of sentiment polarity is introduced, and the model based on sentiment polarity can be represented by: \(y_{t+1}=f(x_{t,1},y_{t})\). We proposed to use the topic similarity between words and microblogs to calculate the sentiment polarity of the microblogs. Since the accuracy of subjective labeling microblogs' sentiment polarity to measure the sentiment polarity highly depends on human factors, we chose to compare the advantages and disadvantages of the method based on the sentiment polarity characteristics extracted by these methods for the predicted performance. Without considering the weight of words, we directly sum up the sentiment polarity value of words. The calculation method is shown in Formula (11). When \(senti_{m}>0\), the microblog expresses a positive sentiment. On the other hand, when \(senti_{m}<0\), the microblog expresses a negative sentiment. When \(senti_{m}=0\), the microblog expresses neutral sentiment. \[senti_{m}=\sum_{n=1}^{N}senti_{n} \tag{11}\]

Table 5 shows the MAPE value of the LR model and the SVR model in predicting movie box office when considering or not considering the sentiment feature. \begin{table}
\begin{tabular}{c c c} \hline \hline  & _Breakup Buddies_ & _Interstellar_ \\ \hline The count of users & 87,787 & 144,221 \\ \hline The highest fans number of users & 13,640,601 & 33,332,635 \\ \hline The users with the most followers & 28 & 47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of users related to the movies.