An Approach to the Study of English and American Literature Using Text Mining and Word Vectors

Interpreting Herman Melville's Typee from the Perspective of Natural Language Processing

Abstract

English and American literature has so far been studied using stylistics from Natural Language Processing, but here it tried to be interpreted from that perspective. The words that writers used in their works are sometimes slightly different in nuance from that used by people in general. The differences in the meanings of words were viewed not sensitively (qualitatively) but quantitatively understood. Herman Melville’s first novel, Typee, was analyzed here. By comparing word vectors of general linguistic data (pretrained model of fastText) with that from texts written by the writer (trained by fastText), the writer’s sense of language was revealed. Based on what has been already studied so far in terms of American Literature, some important words were selected. Therefore, it was confirmed that the differences in the meanings of words were quantitatively demonstrated by the words indicated by their degree of similarity.