首页> 美国卫生研究院文献>other >On the Relation between the General Affective Meaning and the Basic Sublexical Lexical and Inter-lexical Features of Poetic Texts—A Case Study Using 57 Poems of H. M. Enzensberger
【2h】

On the Relation between the General Affective Meaning and the Basic Sublexical Lexical and Inter-lexical Features of Poetic Texts—A Case Study Using 57 Poems of H. M. Enzensberger

机译:情感情感与诗词基本亚词法词法和词间特征之间的关系-以恩·恩斯斯伯格57首诗为例

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

The literary genre of poetry is inherently related to the expression and elicitation of emotion via both content and form. To explore the nature of this affective impact at an extremely basic textual level, we collected ratings on eight different general affective meaning scales—valence, arousal, friendliness, sadness, spitefulness, poeticity, onomatopoeia, and liking—for 57 German poems (“die verteidigung der wölfe”) which the contemporary author H. M. Enzensberger had labeled as either “friendly,” “sad,” or “spiteful.” Following Jakobson's () view on the vivid interplay of hierarchical text levels, we used multiple regression analyses to explore the specific influences of affective features from three different text levels (sublexical, lexical, and inter-lexical) on the perceived general affective meaning of the poems using three types of predictors: (1) Lexical predictor variables capturing the mean valence and arousal potential of words; (2) Inter-lexical predictors quantifying peaks, ranges, and dynamic changes within the lexical affective content; (3) Sublexical measures of basic affective tone according to sound-meaning correspondences at the sublexical level (see Aryani et al., ). We find the lexical predictors to account for a major amount of up to 50% of the variance in affective ratings. Moreover, inter-lexical and sublexical predictors account for a large portion of additional variance in the perceived general affective meaning. Together, the affective properties of all used textual features account for 43–70% of the variance in the affective ratings and still for 23–48% of the variance in the more abstract aesthetic ratings. In sum, our approach represents a novel method that successfully relates a prominent part of variance in perceived general affective meaning in this corpus of German poems to quantitative estimates of affective properties of textual components at the sublexical, lexical, and inter-lexical level.
机译:诗歌的文学体裁本质上与情感的表达和启发有关,既通过内容又通过形式。为了在极其基本的文本水平上探索这种情感影响的性质,我们收集了57种德国诗(“死vertiendigung derwölfe”),当代作家HM Enzensberger曾将其标记为“友好”,“悲伤”或“恶意”。遵循雅各布森()关于分层文本级别生动相互作用的观点,我们使用了多元回归分析来探讨三种不同文本级别(亚词法,词法和词间)对情感特征的特定影响。使用三种类型的预测变量的诗歌:(1)词汇预测变量捕获单词的平均价和唤醒潜力; (2)词间预测器量化词性情感内容内的峰值,范围和动态变化; (3)根据副词层次上的音-义对应关系,对基本情感语气进行副词测量(见Aryani等,)。我们发现词汇预测变量占情感等级差异的主要比例高达50%。此外,词汇间和次词汇间的预测变量占感知到的一般情感含义中很大一部分额外方差。所有使用的文字特征的情感属性加在一起占情感等级差异的43%至70%,而在较抽象的美学等级中仍占23%至48%的差异。总而言之,我们的方法代表了一种新颖的方法,该方法成功地将这首德国诗集中感知到的一般情感含义中的显着变化部分与词法,词法和词间层次上的文本成分的情感特性进行了定量估计。

著录项

相似文献

  • 外文文献
  • 中文文献
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号