# Value Assessment of UGC Short Videos through Element Mining and Data Analysis
## 4 Results
### UGC Short Video Value Assessment
#### 4.3.2 Analysis of Assessment Results  
In this paper, we utilize element mining technology to extract the components of UGC short video value indexes and build the UGC short video value assessment index system. We obtained the UGC short video value assessment results and the weighted ranking results of UGC short video indexes, which are presented in Figures 5 and 6.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Tertiary Indicators** & **CRITIC** & **AHP** & **Portfolio Weights** \\  & **Objective Weights** & **Subjective Weights** & **Portfolio Weights** \\ \hline Fan popularity & 0.0604 & 0.0870 & 0.0564 \\ Creating financial returns & 0.0603 & 0.0663 & 0.0429 \\ Frequency of interaction & 0.0847 & 0.0902 & 0.0820 \\ Information reprint volume & 0.0676 & 0.0657 & 0.0477 \\ Cultural carrying capacity & 0.1049 & 0.0730 & 0.0822 \\ Knowledgeable & 0.1020 & 0.1322 & 0.1447 \\ Skillfulness & 0.0995 & 0.1469 & 0.1568 \\ Cultural identity & 0.1368 & 0.1018 & 0.1495 \\ Community identity & 0.0750 & 0.0535 & 0.0431 \\ Audovisuality & 0.1358 & 0.0764 & 0.1114 \\ Fun & 0.0724 & 0.1064 & 0.0827 \\ \hline \end{tabular}
\end{table}
Table 5: Weighting weights for indicator combinations.  
Based on the principle of UGC short video value assessment, the benefit ratio value (Q) reflects the magnitude of the video's corresponding value, with smaller values indicating higher video value and higher ranking. As shown in Figure 5, the value ranking (using video numbers instead of video names) results for the 20 selected UGC short videos in this paper are as follows:  
\[11>1>6>12>7>13>14>8>9>15>10>16>17>2>3>4>18>5>19>20.\]  
As shown in Figure 6, the final results of the indicator weighting values represent the relative value of UGC short videos, with larger values indicating a higher video value. The  
Figure 5: UGC short video value assessment result chart.  
Figure 6: Bar chart of weighted results of UGC short video indicators.  
ranking of the indicator weighting values for the 20 selected UGC short videos in this paper is as follows:  
\[11>1>6>12>7>13>8>14>9>15>10>16>17>2>3>4>18>5>19>20.\]  
The results indicate that the weighted ranking of UGC short video indicators and the ranking of UGC short video value show a similar trend, with slight differences in the ranking order of individual videos. This suggests that the UGC short video value assessment model proposed in this paper focuses on the mutual influence among the indicators in the UGC short video value assessment system while ensuring evaluation accuracy. The comparison and analysis of the best and worst result items in the assessment object demonstrate the superiority of the VIKOR model in handling multi-attribute decision problems.  
This paper selected 20 UGC short videos, which were categorized into four partitions: game, knowledge, food, and music. To analyze the value performance of UGC short videos in each partition, the weighted quantification results of the videos were divided based on the creator dimension, platform dimension, and user dimension. Radar charts were created using the average values of the three dimensions for each thematic partition, as presented in Figure 7. These radar charts effectively illustrate the value and performance of short videos in different partitions.