Prediction Model of Short Video Danmaku Emotion Recognition

In short video communication, the user's emotional analysis is the basis of exploring the communication law of video media. The short video danmaku contains important information to analyze the user's emotional tendency. So identifying the emotional tendency of the audience via short video danmaku is significant for the video media communication. A novel prediction model for short video danmaku emotion recognition is designed. Firstly, the short video audience's emotions are divided into five categories to construct the user's emotional space. Then the video danmaku is preprocessed by shallow learning model. Then the preprocessed text vector generated by the preprocessor is input into Bi(LSTM+GRU)-Att model to extract high level features. Finally the shallow learning model completes the danmaku emotion recognition. The prediction model not only fully extracts the text and semantic features of the context, but also improves the model's interpretability. The experimental results show that the classification accuracy reaches 87.7% and the accuracy on the test set reaches 99.6%. Therefore this model has high accuracy in short video danmaku emotion recognition. The proposed model can be used as a basic tool for short video propagation mechanism analysis.