# Value Assessment of UGC Short Videos through Element Mining and Data Analysis
## 2 Literature Review
### Research Gap  
In summary, recent years have seen some progress in research assessing the value of UGC short videos, but further improvements can still be made.  
The research methods primarily focus on evaluating the value of UGC short videos in the technical fields of computer vision and signal processing. These methods excessively emphasize the technical elements of UGC short videos while neglecting the content attributes and value attributes of UGC short videos as a form of user-generated content information product. Additionally, value assessment methods based on constructing index systems have started to investigate the content and emotional characteristics of UGC short videos. However, the data modeling-based approach allows for the quantification of various attributes of UGC short videos. Moreover, the machine learning model demonstrates excellent performance in evaluation. This aspect has practical application. Nevertheless, the following problems persist:  
First, the evaluation index system is typically constructed using questionnaires and a literature review. However, it lacks multidimensional value element exploration, correlation analysis, and model representation of UGC short videos through the integration of multiple authoritative data sources. Second, the existing indexes quantify the factors in a singular manner and fail to effectively capture the impact of unstructured data indicators on the value of UGC short videos. This limitation makes it challenging to fully capture the emotional characteristics of users within the indexes. Third, the current evaluation models mostly rely on machine learning techniques, which do not adequately consider the correlation and weightage between different indicators. Consequently, it becomes difficult to comprehensively assess the variations in value among the research subjects. Finally, most of these models are black-box models, and interpreting the evaluation results is often challenging. Thus, additional statistical analysis and visualization methods are required to understand the underlying patterns.  
Considering these issues, this paper aims to address the strong subjectivity of the index system, the insufficient quantification of key features, and the limited interpretation of evaluation results in the evaluation process by mining the value elements of UGC short videos. The research focuses on the following aspects: (1) Mining the value elements for assessing UGC short videos by utilizing text clustering algorithms, visualization technology for theme mapping, and incorporating professional literature and expert commentary texts. This will facilitate the construction of a comprehensive UGC short video value assessment index system; (2) employing a diverse processing approach for multi-dimensional index data: structured data indicators are statistically quantified from UGC short video operation platforms and third-party platforms, while unstructured data indicators, such as user comments, are quantified through LSTM fine-grained sentiment analysis; and (3) utilizing the AHP method and CRITIC method to combine normalized index data for weighting, which is then input into an improved VIKOR model with gray correlation coefficients. This enables the realization of value assessments for UGC short videos.