Multiple Music Sentiment Classification Model Based on Convolutional Neural Network

The network community is a platform for people to communicate. In order to accurately analyze the emotions displayed in music community, this paper proposes a convolutional neural network classification model based on multi-dimensional emotions. Firstly, to solve the problem of feature extraction of emotion words under similar sentence patterns, it proposed a multi-emotion classification method and emotion vector splicing method that conform to music community emotion characteristics. Secondly, aiming at the coexistence of multiple categories of emotions in music comment text, it applied an emotional value measurement method based on music characteristics. Finally, the classification model was constructed with combining methods of emotion vector splicing and emotion value measurement. Through experimental analysis, this model is proved to have good performance in accuracy.