# Value Assessment of UGC Short Videos through Element Mining and Data Analysis
## 1 Introduction  
During the era of Web 2.0, the field of digital media has experienced rapid growth, with various forms of digital media constantly evolving. One such form is UGC short videos, which serve as a prominent means for creating digital resources and engaging in information interaction [1]. UGC short videos refer to short video content created and shared by users on digital platforms. Users typically create these videos to capture life moments, express creativity, share opinions, or showcase their talents [2]. In recent years, UGC short videos have gained significant worldwide success and influence due to their ease of distribution and diverse creativity. This has resulted in the emergence of UGC short video sharing platforms such as YouTube, TikTok, Bilibili, etc. The 2023 China Network Audiovisual Development Research Report, released on 29 March 2023, reveals that as of December 2022, the number of Chinese short video users reached 1.012 billion. This reflects a year-on-year increase of 77 million users, corresponding to a growth rate of 8.3% [3]. However, the increasing number of UGC short videos poses significant challenges for the content management of video sharing platforms. On one hand, the platform witnessesthe widespread dissemination of UGC short videos with negative effects, including defaming others' images, spreading false information, and violating privacy. Users may doubt the platform's content filtering and auditing mechanisms and question the authenticity of the video content, thus harming the platform's reputation and credibility [4]. On the other hand, the platform has overly focused on the traffic generated by UGC short videos, disregarding their inherent value and hindering the development of other videos that offer more creative and valuable content, thereby restricting the diversity of UGC short videos on the platform. Consequently, this limitation hampers the diversity of UGC short videos on the platform, which undermines effective content strategy optimization and hinders improvements in the user experience [5]. Therefore, it is crucial to assess the value of UGC short videos to enhance the operational capacity of the video sharing platform and foster its sustainable development in the future. For example, a user on the TikTok platform uploaded a short video demonstrating how to prepare a specialty dish at home in a vivid and engaging manner. The platform can accurately understand which types of content users are more interested in by evaluating various dimensions. For instance, they might be more interested in cooking skills conveyed through videos or in local Chinese cultural specialties that are popularized by science. This is crucial for platforms to optimize their content recommendation strategies. By adjusting their recommendation algorithms according to users' feedback on different types of food videos, platforms can provide content that better matches users' preferences, ultimately leading to increased user activity and retention. This paper aims to address two questions and provide theories and methods for evaluating the value of user-generated content (UGC) short videos on video sharing platforms in mainland China. (1) What elements contribute to the value of UGC short videos in mainland China? (2) How can we scientifically establish a value assessment model for UGC short videos? This paper proposes a method for assessing the value of UGC short videos. First, the value elements for assessing the value of UGC short videos in the professional field are identified, and an index system for UGC short video value assessment is constructed. Second, the LSTM fine-grained sentiment analysis model is employed to calculate the sentiment value for the unstructured data indexes in the assessment system, achieving index quantification. The quantification of structured data indicators is based on statistics from multi-source platforms, and all indicators are assigned values using a combination of methods.