# Value Assessment of UGC Short Videos through Element Mining and Data Analysis
## 3 Methodology
### Process 2: Quantifying the Assessment Index for UGC Short Video Value  
This paper focuses on quantifying indicators using heterogeneous data from multiple sources. The main sources include structured data from UGC short video operation platforms and third-party platform statistics, as well as unstructured data from user comments. The data processing steps are outlined as follows:  
Step 1: Processing of structured data indicators. To mitigate the impact of indicator data dimensions and enable comparability among different indicators, the indicator data obtained from various platforms should undergo individual maximum-minimum normalization;  
Step 2: Processing of unstructured data indexes. In the social media environment, users express their emotions, opinions, and attitudes through the creation and publication of UGC short videos. These videos encapsulate the intense emotional catharsis and clash of ideas experienced by users [23]. Thus, user attributes related to emotional tendencies become fundamental attributes of UGC short video value [24]. The index data for these user attributes cannot be directly quantified. Typically, quantification is accomplished through text data analysis techniques such as word frequency statistics [25] and hierarchical analysis [26] based on feature words. However, these methods do not originate from the user's inherent attributes, thus making it less likely to explore the user's genuine emotional expressions and emotional tendencies. Consequently, accurate quantification of UGC  
Figure 2: The process of constructing the UGC short video value assessment index system. short video value indexes that encompass user attributes cannot be achieved through these methods. The investigation of user review text sentiment intensity has gained significant attention in the academic, business, and political spheres in recent years. Numerous studies have indicated the crucial role of calculating user review text sentiment intensity in optimizing domain-specific sentiment lexicons [27] and devising brand development strategies. Hence, this paper begins with user review texts and achieves precise quantification of metrics related to user attributes by computing sentiment values extracted from user reviews. It has been observed that computer information technology performs effectively in processing textual information. However, traditional neural network methods can suffer from issues like gradient explosion and gradient disappearance with an increasing number of learning layers. On the other hand, LSTM models exhibit robustness and generalization capabilities [28; 29], which can effectively address these challenges in textual information processing [30; 31; 32]. Therefore, this paper integrates the domain sentiment lexicon with the LSTM deep learning model to calculate the sentiment value of indicators and achieve the quantification of user comment data. The specific steps consist of four parts: data processing, feature word extraction, feature sentence extraction, and training and testing based on the LSTM model. Refer to Figure 3 for detailed information. (1) Data processing. Prior to extracting feature words and special evidence sentences from the comment text, it is necessary to perform data cleaning and de-weighting on the comment data. This step significantly improves the quality and quantity of the comment text while preventing interference or reduction of accuracy in calculating the sentiment value of indicators due to irrelevant or duplicate comments;  
(2) Feature word extraction. Term Frequency (TF) represents the frequency of words, which is the number of times a word appears in the text. The processed result is the word frequency, TF. The importance of a word in the text increases with its frequency. The feature word frequency is obtained by utilizing the counter library to create a candidate library of feature words.