A map of the Urals emotional perception (based on modern regional poetry)

悲伤 诗歌 惊喜 藐视 厌恶 感觉 愤怒 身份(音乐) 价值(数学) 心理学 文学类 美学 社会学 历史 艺术 社会心理学 机器学习 计算机科学
作者
Tatyana Semyan,Evgeny A. Smyshlyaev,Olga Babina,Svetlana Sheremetyeva
出处
期刊:Digital Scholarship in the Humanities [Oxford University Press]
卷期号:37 (4): 1223-1239 被引量:1
标识
DOI:10.1093/llc/fqac007
摘要

Abstract The study of emotional categories in the literary texts of contemporary regional authors and creating a map of the Urals emotional perception based on the data obtained with the Digital Humanities methods is believed to be of great value for solving an important scientific and socio-cultural problems of revealing local specifics and regional identity that faced the Russian society at the turn of the 20th and 21st centuries. For several decades, the modern Ural literature has been a striking socio-cultural phenomenon, numbering more than a hundred writers from different cities (Perm, Yekaterinburg, Chelyabinsk, etc.). One of the key features of the modern Ural poetry is the reflection of regional identity in literary texts. The poems of the Ural writers are full of local toponyms, images of the Urals’ industrial cities and unique nature, as well as of local myths. In this article, a wide range of emotional categories (such as surprise, fear, anger, disgust, joy, contempt, sadness, love, etc.) in modern poetry is investigated based on the emotional models by the American psychologists Robert Plutchik (Emotion: Theory, Research, and Experience, Vol. 1: Theories of Emotion. New York: Academic), Carroll Izard (Izard, C. E., 2012, The Psychology of Emotions. New York: Plenum), and Paul Ekman (Ekman, P., 2007, Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. New York: Holt Paperbacks). The purpose of the research is to discover what emotions prevail in modern poetic texts dedicated to the Ural region by analysing how literary works absorb and critically rethink the space of the Urals. A comprehensive research methodology is proposed that combines a qualitative study of the literary material and automated quantitative-digital analysis of corpus data with the subsequent visualization of the results.
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