The enduring comparison between Li Bai ((sic)(sic), 701-762) and Du Fu ((sic)(sic), 712-770), two towering poets in Chinese literary history, has traditionally centred on semantic and thematic aspects, often overlooking the phonetic dimension or narrowly focusing on rhythm and rhyme while neglecting other sound features. This study applies sound symbolism principles to explore the emotional and perceptual effects of sounds in their poetry, focusing on a quantitative phonetic comparison of their works. Utilising character sound information extracted from the rhyme book Guangyun ((sic)(sic) extended rhymes), their poems were transformed into sound vectors for authorship attribution by machine learning models. Several of these models achieved average F1 scores above 80%, effectively demonstrating the capability of sounds to distinguish between the two poets. Through statistical analyses, their preferred phonetic features were located and the sound-sentiment relationships in their poetry were identified, revealing that Li Bai favoured the more positive sound features while Du Fu showed an inclination towards the more negative ones. Moreover, the distinct sound preferences sculpted Li's poetry with a vibrant, melodious, and unbound quality, while lending a subtle, sombre, and constrained undertone to Du's verses. Focusing on the phonetic aspect, this study takes a novel digital humanities approach to the traditional Li-Du comparison topic, offering fresh insights into their differences and underscoring the impact of sounds on the aesthetic experience of poetry.
原文链接
分享
引用
