# 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  
The influence of microblogs is reflected in the user's response behavior to microblogs, including reading behavior, such as behavior, comment behavior, and forwarding behavior. However, the influence of microblogs may be different. When the user has many fans, the microblogs posted by the user will be read by more people, which can enhance the influence of the microblogs. When there are many likes to a microblog, we can infer that the microblog is likely to be influential on the microblogging platform. In this paper, we use Formula (6) to measure the influence of a microblog. Here, \(FC\) means the number of fans, \(PC\) is the count of praises, \(CC\) is the comment count, and \(RC\) is the count of re-posts. \[influ_{m}=\ln(FC_{m}+1)+PC_{m}+CC_{m}+RC_{m} \tag{6}\]  
In order to reduce the over-impact of a large count in Formula (6), we use \(norminflu_{m}\) to normalize \(influ_{m}\) into a value range \([0,1]\). The normalization method is shown in Formula (7). \[norm\_influ_{m}=\frac{1}{1+e^{-\tan^{-1}{(influ_{m})}}} \tag{7}\]  
Note that the sentiment polarity of a microblog also contributes to the social influence of the microblog. For example, a piece of bad news always spreads fast on social networks. Therefore, this paper proposes combining microblog influence and sentiment polarity to evaluate the social influence of a microblog, which is shown in Formula (8). \[\mathit{sif}_{m}=\left\{ \begin{array}{cc}(1+\mathit{senti}_{m})\ *(1+norm\_influ_{m})\ \mathit{senti}_{m}>0\\ (1-\mathit{senti}_{m})\ *(1+norm\_influ_{m})\ \mathit{senti}_{m}<0\\ 1*(1+norm\_influ_{m})\ \mathit{senti}_{m}=0\end{array} \right. \tag{8}\]  
This paper considers the influence scores of microblogs with three sentiment polarities and calculates the influence of one day as the influence feature for information prediction. The influence combines positive scores, negative scores, and neutral scores, which is defined by Formula (9). Here, \(pos\_sif_{d}\) is the sum of \(\mathit{sif}_{m}\ (\mathit{senti}_{m}>0)\) within the day \(d\), \(neg\_sif_{d}\) is the sum of \(\mathit{sif}_{m}\ (\mathit{senti}_{m}<0)\) within the day \(d\), \(neu\_sif_{d}\) is the sum of \(\mathit{sif}_{m}\ (\mathit{senti}_{m}=0)\) within the day \(d\), and \(\mathit{total\_sif}_{d}\) is the sum of \(\mathit{sif}_{m}\) within the day \(d\). \[\mathit{inf}_{d}=\frac{2*pos\_sif_{d}-neg\_sif_{d}+neu\_sif_{d}}{total\_sif_{d}} \tag{9}\]  
To sum up, the extraction algorithm of the microblog influence feature is shown in Algorithm 2. ```
1:\(\mathit{sif}_{m}=0\)
2:\(\mathit{sif}_{m}=0\)
3:\(\mathit{sif}_{m}=0\)
4:\(\mathit{sif}_{m}=0\)
5:\(\mathit{sif}_{m}=0\)
6:\(\mathit{sif}_{m}=0\)
7:\(\mathit{sif}_{m}=0\)
8:\(\mathit


```
1:The set of all the related microblogs in one day, M; The sentiment of each microblog, \(senti_{m}\)
2:Influence of each microblog,\(norminf1_{m}\)
3:Influence Feature, \(inf\)
4:possis, negsis, neusis, totalsis \(\gets 0\)
5:for each \(microblog_{m}\) in \(M\)do
6:\(norminflu_{m}\leftarrow\)computed by Formula (7)
7:if\(senti_{m}>0\)then
8:\(sif_{m}\leftarrow(1+senti_{m})\ *(1+norm\_influ_{m})\)
9:\(pos\_sif\)\(\leftarrow\)pos\(\_sif\)\(+sif_{m}\)
10:elseif\(senti_{m}\)\(<0\)then
11:\(sif_{m}\leftarrow(1-senti_{m})\ *(1+norm\_influ_{m})\)
12:\(neg\_sif\)\(\leftarrow\)neg\(\_sif\)\(+sif_{m}\)
13:else
14:\(sif_{m}\gets 1\ *(1+norm\_influ_{m})\)
15:\(neu\_sif\)\(\leftarrow\)neu\(\_sif\)\(+sif_{m}\)
16:endif
17:\(total\_sif\)\(\leftarrow\)total\(\_sif\)\(+sif_{m}\)
18:endfor
19:\(inf\leftarrow\frac{2*pos\_sif-neg\_sif+neu\_sif}{total\_sif}\)
20:returninf
```

**Algorithm 2**_Influence Feature Extraction_

After extracting the prediction features, we need to find a suitable model to describe the relationship between input (features) and output (real-world information). Therefore, this paper selects the following two regression models to test the prediction ability of microblogs. (1) Linear Regression Model (LR). The existing research on microblog-based information prediction usually assumes a linear relationship between variables.