# Dynamic Scene Segmentation and Sentiment Analysis for Danmaku
## 4 Experiments
### Model Training
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Original Dictionary**} & \multicolumn{2}{c}{**Expanded Dictionary**} \\ \cline{2-5}
**Model** & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline WoBert & 83.43 & 83.92 & 84.01 & 84.23 \\ MacBert & 83.85 & 84.09 & 84.67 & 85.11 \\ MacBert-CNN & 87.23 & 87.91 & 88.23 & 89.19 \\ MacBert-GCN & 87.56 & 88.25 & 87.99 & 88.31 \\ MacBert-BiLSTM & 88.19 & 89.52 & 89.04 & 89.68 \\ MacBert-RNN & 89.11 & 89.42 & 90.52 & 90.58 \\ MacBert-RCNN & 90.03 & 91.39 & 91.42 & 92.13 \\ RoBERTa-CNN & 91.08 & 91.93 & 92.08 & 92.47 \\ RoBERTa-RNN & 92.79 & 93.24 & 93.49 & 94.10 \\ RoBERTa-BiLSTM & 91.58 & 93.42 & 94.30 & 94.70 \\ Danmaku-E & 94.58 & 94.92 & 95.37 & 95.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of experimental results of models (%). \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Parameter** \\ \hline Batch size & 64 \\ Vocab size & 21,128 \\ Maximum sequence length & 128 \\ Optimizer & adamw\_torch \\ Scheduler & linear \\ Seed & 42 \\ Learning rate & \(5\times 10^{-5}\) \\ Warmup ratio & 0.2 \\ Epoch & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter Settings. unusual language somewhat well, our special word list remains important for covering all platform-specific terms. The biggest performance drop came when we removed the fuzzy feature layer (3.22% accuracy, 3.26% F1-score). Recall suffers most (3.29% decrease). Without this layer, the model has trouble with mixed emotions like sarcasm and cannot effectively tell similar feelings apart. The confusion matrix shows 12% more mistakes between emotions like sadness and fear. These results support our design decisions. The scene merging method helps segment videos correctly. The special word list covers internet language, while the fuzzy layer handles complex emotions. Since removing the fuzzy layer hurt performance the most, it is clearly our most important innovation for analyzing Danmaku comments. Danmaku-E is used to subdivide the sentiment categories of Danmaku comments into seven distinct categories, providing a nuanced understanding of user emotions expressed in real-time comments. The model's classification capabilities enable more precise sentiment analysis, which is crucial for accurately interpreting the emotional undertones of user interactions. Seven example comments and their corresponding sentiment categories are showcased in Table 6.  
In the processing phase, the model conducts a thorough semantic analysis of each word within a comment. It calculates the membership of each word in different sentiment categories by integrating information from the sentiment dictionary and contextual data. To clarify how the sentiment classification boundary is determined, we can use \"Bad Era\" as an example. Initially, the model uses BERT to transform the entire comment into a vector representation, effectively capturing its semantic and contextual nuances. Following this, the fuzzy feature layer assesses the membership level of the comment within various sentiment categories using a membership function. In the case of \"Bad Era\", the model combines \"Bad\" and \"Era\" with the sentiment dictionary to compute the membership function values. According to the fuzzy rules, since the membership for \"Malice\" is high and that for \"Sadness\" is medium, \"Bad Era\" is categorized under \"Malice\". \begin{table}
\begin{tabular}{c c} \hline \hline
**English Text** & **Emotional Label** \\ \hline Movie Really Very Great & Benefence \\ Happiness Most Important & Happiness \\ Ah Actually Have This & Surprise \\ Bad Era & Malice \\ Soul All Be Scared & Fear \\ Workers Angry & Anger \\ The Whole Play The worst & Sadness \\ \hline \hline \end{tabular}
\end{table} 
Table 6: An Example of the Correlation between Danmaku Text and Emotional Tags.