# Dynamic Scene Segmentation and Sentiment Analysis for Danmaku
## 5 Conclusions  
In this paper, we present an innovative method that combines scene segmentation techniques with Danmaku sentiment analysis. This method effectively tackles significant challenges in this research area. Compared with sentiment analysis models based only on text, Danmaku-E achieves remarkable improvements in sentiment analysis accuracy and F1 score. When using the extended sentiment lexicon, its accuracy is 95.37% and its F1 score is 95.66%, which has a significant improvement over other models in terms of performance indicators. We further validate the contributions of each component within our model by conducting an ablation study. Removing the scene segmentation feature results in a 2.16% decrease in accuracy and a 2.21% drop in the F1 score. Excluding the extended sentiment dictionary leads to a smaller decrease in accuracy (0.79%) and F1 score (0.74%). The most significant performance drop is observed when the fuzzy feature layer is removed, resulting in a  
Figure 6: An example of a rule network diagram of video segments and emotional labels. Figure 7: An example of visualization of association rules between users and video segments. 3.22% decrease in accuracy and a 3.26% decrease in the F1 score. These findings highlight the importance of each component within our model, supporting our design choices and demonstrating the robustness of our approach. In addition to these improvements, the Apriori algorithm successfully identifies meaningful association rules between video content and Danmaku comments. In our experiments, we identify strong correlations between specific video segments and emotional responses. These correlations provide valuable insights into user behavior and content dynamics, enabling more accurate predictions of audience emotional tendencies. Despite its strengths, the method has some limitations. The complexity of sentiment classification presents several challenges. Sentiment expressions in Danmaku comments are often intricate and diverse, with some comments simultaneously exhibiting multiple sentiment tendencies. When processing such mixed emotions, our model may demonstrate inaccuracies in classification. For instance, certain comments may contain both positive and negative sentiments, but the model might only recognize one sentiment tendency, potentially leading to biased results. Regarding dataset limitations, the dataset utilized in this study predominantly originates from a specific video platform, which may not comprehensively represent the emotional expressions inherent to Danmaku across other platforms or different video types. Consequently, this limitation might impact the model's generalization capability when applied to broader contexts or diverse scenarios. The accuracy of scene segmentation also poses challenges. Although methods based on color histogram similarity and temporal features are employed to enhance segmentation precision, errors may persist in certain complex scenes, such as unclear scene transitions or gradual fades. These segmentation inaccuracies may adversely affect the subsequent sentiment analysis outcomes. Furthermore, balancing model complexity with computational efficiency remains an issue. While the model excels in sentiment analysis accuracy, its computational complexity is relatively high. Specifically, when handling large-scale datasets, challenges such as extended processing time and substantial memory consumption may arise. This compromises the model's applicability in real-time sentiment analysis scenarios, limiting its effectiveness in time-critical applications. In summary, while our model offers substantial improvements in sentiment analysis for Danmaku comments, it is essential to recognize these limitations. Future work will focus on addressing these challenges by exploring more sophisticated methods for handling mixed emotions, expanding the diversity of training datasets, improving scene segmentation accuracy, optimizing model efficiency, and continuously updating the sentiment lexicon. Conceptualization, J.J.; Methodology, L.L.