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Although lyrics represent an essential component of music, few musicinformation processing studies have been conducted on the characteristics oflyricists. Because these characteristics may be valuable for musicalapplications, such as recommendations, they warrant further study. Weconsidered a potential method that extracts features representing thecharacteristics of lyricists from lyrics. Because these features must beidentified prior to extraction, we focused on lyricists with easilyidentifiable features. We believe that it is desirable for singers to performunique songs that share certain characteristics specific to the singer.Accordingly, we hypothesized that lyricists account for the uniquecharacteristics of the singers they write lyrics for. In other words,lyric-lyricist classification performance or the ease of capturing the featuresof a lyricist from the lyrics may depend on the variety of singers. In thisstudy, we observed a relationship between lyricist-singer entropy or thevariety of singers associated with a single lyricist and lyric-lyricistclassification performance. As an example, the lyricist-singer entropy isminimal when the lyricist writes lyrics for only one singer. In ourexperiments, we grouped lyricists among five groups in terms of lyricist-singerentropy and assessed the lyric-lyricist classification performance within eachgroup. Consequently, the best F1 score was obtained for the group with thelowest lyricist-singer entropy. Our results suggest that further analyses ofthe features contributing to lyric-lyricist classification performance on thelowest lyricist-singer entropy group may improve the feature extraction taskfor lyricists.

Lyricist-Singer Entropy Affects Lyric-Lyricist Classification Performance