# Value Assessment of UGC Short Videos through Element Mining and Data Analysis
## 3 Methodology  
This paper aims to assess the value of UGC short videos. It utilizes authoritative texts, including literature collections in the field and expert commentary articles, as the basis. Text clustering algorithms and visualization techniques are employed for topic mapping. Through this process, the UGC short video value elements are identified, and a comprehensive UGC short video value assessment index system is constructed. Subsequently, a UGC value assessment model is established to achieve effective value assessment of UGC short videos, as illustrated in Figure 1.  
Process 1: Constructing an Index System for Assessing the Value of UGC Short Videos Based on Element Mining  
This study initially retrieves relevant literature on UGC short video value assessment from databases such as CNKI, Wanfang, and WOS. The collected literature includes fields such as title, abstract, keywords, and publication time. Additionally, expert review texts related to UGC short videos are collected as Supplementary Data. The collected data undergoes pre-processing, which involves text data normalization, merging, and the removal of duplicate and invalid samples. As a result of this process, a dataset for UGC short video value mining is obtained.  
Text clustering algorithms and visualization techniques, such as topic mapping, are employed to mine UGC short video value elements and construct the UGC short video value evaluation index system. The specific process is illustrated in Figure 2.  
After pre-processing the data, the text dataset undergoes entity and inter-entity relationship extraction. Specifically, this process includes: (a) The extraction of entities. This paper performs data cleaning on the text and employs the jieba word splitting tool to accurately segment the text data based on specific word splitting patterns, thereby eliminating redundant data. Additionally, the TF-IDF strategy in natural language processing is utilized to remove punctuation and stop words, minimizing interference with word segmentation and obtaining the subject words for each analyzed content; (b) The calculation of inter-entity relationships. The K-means method is utilized to cluster the subject words, and then the strength of entity association, inter-entity spacing, and inter-entity position are calculated. The inter-entity association relationship is visually presented by controlling the entity color; and (c) The construction of topic mapping. The topic mapping is constructed using VOSviewer to visualize the association relationship between elements.  
Figure 1: UGC short video value assessment model.  
Subsequently, theme clustering and key element mining are conducted. This process includes: (a) The analysis of clusters. Utilizing the constructed topic mapping, cluster analysis and representative entity word extraction are performed to analyze the characteristics of entity words within each cluster category; and (b) The mining of key elements. The clusters that fulfill the value assessment criteria for UGC short videos are selected, and these selected clusters are renamed by incorporating the value characteristics of UGC short videos.  
Lastly, utilizing classical theory or a priori knowledge, the mined value assessment elements are classified and organized hierarchically. The structure is then optimized and adjusted based on the similarity and difference of indicators, resulting in the construction of a UGC short video value assessment index system.