Abstract
The purpose of this article is to examine the linguistic differences between poems written by ‘amateurs’ and those written by ‘professionals’ and then to use these characteristics to rank a number of contemporary American poems. The corpus of poems used consist of 100 poems randomly selected from a recent anthology of professional poets and a control group of 100 poems written by amateurs. The poems were reduced to ninety-eight linguistic and psycholinguistic variables, and these were used in a machine learning algorithm to build an ensemble classifier. The accuracy of the classifier was 84.5%. The probability scores of the individual poems was then used to rank the professional poems on a continuum representing amateur at one extreme and professional at the other, thereby providing an objective means of ranking contemporary poems.
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