Fuzzy Semantic Agent Based on Ontology Model for Chinese Lyrics Classification

Nowadays, social media is getting more and more popular so that many people choose to absorb the knowledge, share their moods, read news, listen to music, and appreciate the video on the Internet. The popular Chinese songs can be categorized according to their song style, their released decade, their singer, and so on. Currently, the song is always classified as a single category, such as inspiration, love, or family. However, when people listen to a song, they will have a different feeling according to their moods in the moment. This paper adopts the lyrics of the popular Chinese songs on the Internet as the experimental samples. Then, we classify the songs based on the natural language processing, ontology, Word2Vec, and fuzzy inference mechanism. The adopted natural language mechanism contains term comparison and term similarity to compute the different-category weights. Additionally, we also use predefined ontology, knowledge base, and rule base to classify the songs. Moreover, we also adopt the multilayer perceptron neural network with the backpropagation algorithm to train the data under a supervised learning. The learned results are better than the ones of the fuzzy inference mechanism. In the future, this study will enhance ontology, knowledge base, and rule base as well as enlarge the number of experimental samples to improve the performance. Finally, we will combine music appreciation with the robot to make children learn the knowledge more interesting.