Abstract
The number of songs are increasing at a very high rate around the globe. Out of the songs released every year, only the top few songs make it to the billboard hit charts .The lyrics of the songs place an important role in making them big hits combined with various other factors like loudness, liveness, speech ness, pop, etc. The artists are faced with the problem of finding the most desired topics to create song lyrics on. This problem is further amplified in selecting the most unique, catchy words which if added, could create more powerful lyrics for the songs. We propose a solution of finding the bag of unique evergreen words using the term frequency-inverse document frequency (TF-IDF) technique of natural language processing. The words from this bag of unique evergreen words could be added in the lyrics of the songs to create more powerful lyrics in the future.
Highlights
IntroductionMusic influences a person on a neurological level
The term frequency-inverse document frequency (TF-IDF) scores for the respective lyrics were calculated to find the important words in each song
This word feature selection was based upon selecting words having TF-IDF score greater than the minimum threshold of 0.2
Summary
Music influences a person on a neurological level It impacts the temperament and conduct of a person [1]. It influences the function of the brain and human actions including, relieving stress, depression symptoms, as well as enhancing cognitive and motor functions, spatial-temporal learning and neurogenesis which is the capacity of the brain to create neurons [2]. The problem is further intensified in selecting the most unique, catch words which if added, could create more powerful lyrics for the songs. These powerful lyrics with the right combination of other music features (liveliness, loudness, speech ness, etc.) could create hit songs in the future
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