An Analysis of Music Lyrics by Measuring the Distance of Emotion and Sentiment

The present world has an oversupply problem in regard to current products. In order to the solve oversupply problem, we are breaking down the trade barriers between countries. However, in the future, it can be an era of breaking down individual trade barriers of a person and resolving oversupply of goods. This means that it will be an important issue in analyzing the characteristics of each person and providing the goods that match them. Music is an invisible and intangible product even though we can see this as a commodity as well, meaning that the music market is also a time period of oversupply because the paradigm has changed from analog to electronic music. In other words, the time it takes to make music has been shortened radically. After the paradigm shifted to the era of electronic music, the value of music streaming market exploded. In other words, it was a concept of music ownership in the past, but now it can be said that it has turned into a concept of approaching music. The market value of approaching concepts such as streaming is exploding. In order to overcome the oversupply problem and to recommend it, it is obviously a limit to distinguish music as the current profile elements such as genres, titles, and artists. So we work on classifying on of the greatest features of music: the lyrics. Since the lyrics of music consist of texts, music can be considered to contain human emotions because humans can convey their feelings in texts. We can use sensibility of the lyrics of music to classify music. We used about 11,000 lyrics based on the Billboard Chart from Hot 100. The Natural Language Processing (NLP) work was done to refine each of the lyrics, and the music was digitized as sentiment and emotion. After selecting a new song using the K-Nearest Neighbor (K-NN) algorithm, we propose a method for recommending music with the most similar lyrics. In conclusion, we can inform users of songs with lyrics that are most similar to the overproduction of music.