# 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
The feature words are classified into the UGC short video value assessment index system keyword table through manual screening, with reference to the constructed UGC short video value assessment index system;  
(3) Feature sentence extraction. After performing word-by-word traversal using jieba splitting, the Stanford NPL is utilized for dependency sentence analysis. The result is then compared with the keyword table of the UGC short video value assessment index system. The matching feature words are considered the feature attributes of the comment text, thus extracting the comment as a special evidence sentence;  
Figure 3: Emotional intensity calculation model based on the LSTM model. (4) LSTM-based model training and testing. To reduce the manual sentiment labeling time, this study labels the extracted idiosyncratic sentences with sentiments (\(-1\) for negative, 0 for neutral, and 1 for positive) using a sentiment dictionary. The Boson NLP sentiment lexicon, constructed based on microblogging, news, forums, and other data sources, serves as the base lexicon, supplemented by the KnowNet HowNet negative word and degree adverb lexicon, ultimately forming the domain sentiment lexicon. For model training and testing, the Long Short-Term Memory (LSTM) neural network is used. First, the data is divided into a training set and a test set with a ratio of 4:1. Second, the tanh function is chosen as the activation function for the LSTM model, commonly used for transforming input continuous real values into output values between \(-1\) and 1. The word vector dimension is set to 100, representing the semantic information of each word as a 100-dimensional vector. The data batch size is 32, ensuring computational efficiency and providing sufficient data for the model to learn. To prevent overfitting, the dropout technique is introduced, randomly disabling a portion of neurons during training. After cross-validation, a dropout value of 0.5 is selected as it generates the most diverse network structures;  
Step 3, the index data obtained from the normalization process in Steps 1 and 2 are combined together to form the UGC short video value index matrix;  
Step 4, the combination of indicators is determined using the AHP method and the CRITIC method. This approach integrates the subjective experience of decision-makers with the objective attributes of data information. Hierarchical analysis (AHP) is a multi-criteria decision-making method introduced by [PERSON], an American operations researcher [33]. This method simplifies multi-objective decision-making problems and enables fuzzy quantification of qualitative indicators, making it simple and practical. However, it heavily relies on subjective judgment and does not offer novel solutions for decision-makers, thus being categorized as a subjective empowerment method [34]. The CRITIC method, proposed by [PERSON] et al. in 1995 [35], is an objective empowerment method. It provides a comprehensive assessment of the objective weights of indicators by considering the comparative strengths of evaluation indicators and the conflicts between them. However, the objective weighting method demands a significant amount of data, involves complex calculations, and does not consider the rater's importance, leading to a notable discrepancy between the assessed weights and the actual importance [36]. In this study, the AHP method and the CRITIC method are integrated with subjective and objective assignments to conduct a comprehensive assessment of the weights of evaluation indices. This approach addresses the limitations of single assignment methods [37; 38] and provides comprehensive weights for each index. It effectively incorporates both the subjective intentions of decision-makers and the objective attributes of data information. The subjective weight of the \(j\)th indicator calculated using the AHP method is denoted as \(W_{Pj}\), and the objective weight of the \(j\)th indicator calculated using the CRITIC method is denoted as \(W_{Vj}\), assuming a total of \(i\) indicators.