Ullrich Susann, Aryani Arash, Kraxenberger Maria, Jacobs Arthur M, Conrad Markus
Languages of Emotion, Freie Universität BerlinBerlin, Germany; Department of Experimental and Neurocognitive Psychology, Freie Universität BerlinBerlin, Germany.
Languages of Emotion, Freie Universität BerlinBerlin, Germany; Max Planck Institute for Empirical AestheticsFrankfurt am Main, Germany.
Front Psychol. 2017 Jan 11;7:2073. doi: 10.3389/fpsyg.2016.02073. eCollection 2016.
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 scales-valence, arousal, friendliness, sadness, spitefulness, poeticity, onomatopoeia, and liking-for 57 German poems ("") which the contemporary author H. M. Enzensberger had labeled as either "friendly," "sad," or "spiteful." Following Jakobson's (1960) 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 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 according to sound-meaning correspondences at the sublexical level (see Aryani et al., 2016). 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 . 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 in this corpus of German poems to quantitative estimates of affective properties of textual components at the sublexical, lexical, and inter-lexical level.
诗歌这一文学体裁在本质上与通过内容和形式来表达和引发情感相关。为了在极其基础的文本层面探究这种情感影响的本质,我们收集了当代作家H.M. 恩岑斯贝格尔标注为“友好”“悲伤”或“恶意”的57首德语诗歌在八个不同量表上的评分,这些量表包括效价、唤醒度、友好度、悲伤度、恶意度、诗意、拟声词和喜爱度。遵循雅各布森(1960)关于文本层次生动相互作用的观点,我们使用多元回归分析,通过三种类型的预测变量来探究来自三个不同文本层次(次词汇、词汇和词汇间)的情感特征对诗歌感知的具体影响:(1)捕捉单词平均效价和唤醒潜力的词汇预测变量;(2)量化词汇情感内容内的峰值、范围和动态变化的词汇间预测变量;(3)根据次词汇层面的音义对应关系得出的次词汇测量指标(见阿里亚尼等人,2016)。我们发现词汇预测变量能够解释高达50%的情感评分方差中的大部分。此外,词汇间和次词汇预测变量在感知中解释了很大一部分额外方差。所有使用的文本特征的情感属性共同解释了情感评分方差的43 - 70%,并且在更抽象的审美评分方差中仍占23 - 48%。总之,我们的方法代表了一种新颖的方法,成功地将这一德语诗歌语料库中感知方差的一个显著部分与次词汇、词汇和词汇间层面文本成分情感属性的定量估计联系起来。