# Dynamic Scene Segmentation and Sentiment Analysis for Danmaku
## 2 Related Works
### Deep Learning-Based Emotion Analysis  
Deep learning methods have been used with success for analyzing emotions. These include CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), and attention mechanisms [5]. They can find complex emotional signals and features of meaning. [PERSON] and [PERSON] [6] came up with a sentiment analysis method for film review texts. Their method is based on SVM (Support Vector Machines) classification technology in sentiment dictionaries and machine learning. First, they built basic sentiment dictionaries, dominant sentiment dictionaries, negative word dictionaries, and degree adverb dictionaries. Then, they combined these four dictionaries to expand the dictionary. Next, they constructed the SVM model training set by calculating the combination of sentiment weights and user scores. Finally, they used test data for sentiment classification experiments. The results showed that their method has a higher accuracy in sentiment classification compared to methods based on basic sentiment dictionaries. This proves the effectiveness of their proposed emotion-aware system in text sentiment analysis and offers a new way of thinking for emotion analysis in the film review field. [PERSON] and [PERSON] [7] did not directly study how to solve the long-distance dependency problem. Instead, they introduced the BERT pre-training model. BERT was designed to pre-train deep bidirectional representations from unlabeled text. It depends on both left and right contexts in all layers during pre-training. BERT's pre-training mainly involves two tasks: Masked Language Modeling and Next Sentence Prediction. After pre-training, the BERT model only needs to add an extra output layer for fine-tuning. Then, it can be used to create state-of-the-art models for many natural language processing tasks, like question answering and language inference, without making many changes to the architecture for specific tasks. BERT has achieved great results in many natural language processing tasks. For example, it increased the GLUE score to 80.5 (an absolute increase of 7.7 points), the MultiNLI accuracy to 86.7% (an absolute increase of 4.6%), the SQuAD v1.1 question answering test F1 value to 93.2 (an absolute increase of 1.5 points), and the SQuAD v2.0 test F1 value to 83.1 (an absolute increase of 5.1 points). Although BERT is not a method used specifically for solving the long-distance dependency problem, its strong ability to learn context information provides strong support for solving various natural language processing problems, including long-distance dependency. Future research can explore ways to solve the long-distance dependency problem based on BERT. In recent years, BERT has achieved notable success in natural language processing research. [PERSON] et al. [8] demonstrated that BERT pre-trains deep bidirectional representations by jointly conditioning on both left and right contexts across all transformer layers. The pre-training of BERT primarily involves two tasks: Masked Language Modeling and Next Sentence Prediction. RoBERTa (Robustly Optimized BERT Approach) retains the original BERT architecture while introducing refinements to fully exploit BERT's capabilities. [PERSON] et al. [9] conducted detailed comparisons of various components within BERT, such as masking strategies and training steps, improving BERT's performance by extending the training time using larger batch sizes and longer sequences on a wider range of datasets, and by eliminating the next sentence prediction task and adopting the key method of dynamic masking. MacBERT builds upon both BERT and RoBERTa, replacing the original MLM (Masked Language Modeling) task with MLM as a correction (Mac) task to mitigate discrepancies between pre-training and finetuning. [PERSON] et al. (2020) revisited previously popular pretrained language models and adapted them to Chinese. In recent years, deep learning has provided powerful tools for sentiment analysis, specifically in the domain of Danmaku comments.