TWO.ZERO.ONE.ZERO - Lyrische Interpretation (German Edition)

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At the sublexical level, the absolute value of the arousal level of all codas in the text seems to be the strongest predictor. Thus, any coda's arousal value being significantly different from the distribution's mean—no matter whether it is especially low or high-arousing—leads to higher sadness ratings.

The same holds for the valence values of all types of salient segments in the poems. Furthermore, the occurrence of many salient nuclei in a text goes along with lower sadness rating. Bivariate correlations between sadness ratings and four predictors: Both types of arousal show a negative partial correlation with the dependent variable: Bivariate correlations between liking ratings and two predictors: For the dependent variable poeticity , lexical arousal appears as a highly significant predictor variable if its absolute values are considered: The more deviant the lexical arousal values are from zero, no matter whether into a higher arousing or more calming direction, the less poetic the poem is rated Figures 8A,B.

Thus, poems that contain predominantly words of a rather unremarkable arousal—not significantly high- or low-arousing—are perceived as more poetic than poems with salient lexical arousal features. Moreover, the poeticity ratings are also strongly influenced by sublexical affective values. This results from the finding that the continuous arousal values of the nuclei are negatively correlated with the poeticity ratings, while the absolute arousal values correlate in a positive manner.

Thus, for the negative range—namely the low-arousing part—the inferred statement is the same, whereas in the positive—high-arousing—range the correlation patterns oppose and hence zero out each other. Consequently, more arousing nuclei values do not necessarily lead to diminished poeticity ratings. Bivariate correlations between poeticity and four predictors: The onomatopoetic perception is significantly influenced by variables from all three text levels.

At the lexical level, a higher occurrence of negatively valenced words in a poem leads to increased onomatopoeia ratings. In contrast, with a higher maximum value of lexical valence in a poem, the ratings for onomatopoeia become slightly higher as well. However, this partial correlation is not a very strong one. Regarding the spread of lexical valence and arousal in each poem—depicted by their standard deviations—higher deviations involve lower onomatopoeia ratings Figures 9A,B. At the sublexical level, the nuclei seem to play an important role: The overall picture receives further complexity by the fact that a more positive valence specifically of salient codas augments the onomatopoeia ratings.

Bivariate correlations between onomatopoeia ratings and four predictors: In summary, it can be stated that in all of the regression models at least two out of the three examined levels of affective text analysis contribute significantly but differently to the variance in the respective dependent rating variable.

In eight out of nine cases, at least one of the lexical variables valence or arousal is contained in the regression model, in six cases it enters the model first. Especially the inclusion of lexical arousal in seven models increases the amount of explained variance to a noticeable extent. Lexical valence supports four models significantly.

The newly defined inter-lexical variables, whose task it is to represent dynamic shifts and spreads of affective lexical content, find their way into the regression equations in five out of nine models. From the huge number of sublexical predictor variables, prominently the arousal level of salient segments consistently explains variance in eight out of nine models.

In addition, the pure number of salient segments in a poem, disregarding their affective values, plays a role in four of the nine regression models. This study investigates to which extent affective connotations at the rather basic textual dimensions of phonological units and single words or the relative positions of the latter influence the overall affective perception of poetry.

To estimate their affective perception by the reader, we collected ratings of the poems on several affective scales, ranging from the basic dimensions valence and arousal to the author-based discrete affective dimensions friendliness, spitefulness, and sadness, to aesthetic evaluations of poeticity and liking, as well as the concept of onomatopoeia. To identify basic textual sources potentially determining these ratings we quantified affective properties of the texts using valence and arousal values from large-scale normative lexical databases at three different basic text levels: We then used these measures as predictor variables in a stepwise multiple regression approach to test how much of the variance in the perceived general affective meaning can be accounted for by these textual variables, and how these influences may vary across different rating dimensions.

Overall, our results from the different regression models show that a prominent portion of the variance in affective and further aesthetic and onomatopoetic ratings of our poems can be accounted for by affective features at the sublexical, lexical, and inter-lexical level.

These findings suggest that very basic affective processes play a crucial role in poetry perception. Note that we do not argue that higher-level processes would not matter, they are just not studied in our approach. The best predictors of the perception of the general affective meaning of the poems—assessed via ratings—were the average lexical valence and arousal values of words—in terms of their deviation from an expected average value—contained in the poems.

Pragmatically speaking, this would mean that it is sufficient to put words with specific affective connotations together to create half of the affective impact a poem is able to provoke in the reader. Again, while this view may appear extremely minimalistic, it is well in line with other findings from reading studies using normal sentences or passages from novels Anderson and McMaster, ; Whissell et al.

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Beyond the single word level, our study provides a number of novel results for inter-lexical phenomena and how they contribute to the affective reading experience. From a neuroscientific perspective, Hsu et al. In our data, for instance, the overall ratings of arousal induced by a poem were best predicted not by the average lexical arousal values but rather by specific maxima of lexical arousal. The maximum lexical arousal value in a text is a mathematical constituent of the arousal span max—min and probably the most relevant one, as it represents salient peaks or particularly exciting moments in a text—which well fits the general view on this emotion dimension as an alert system reacting immediately to salient affective input.

Such findings underline the importance of deviation from expected patterns—here the outstanding arousal level of one single word in a text—for foregrounding effects compare with the Neurocognitive Poetics model, NCPM, Jacobs, , a. Furthermore, our novel operationalization of the evolution of affective content throughout a text—correlating lexical affective values with word position—yields a number of interesting results: Instead, poems were perceived as more friendly when words of an increasingly positive character were used toward the final lines of a poem.

We conclude that these correlations between word positions and affective values offer a good proxy for how overall affectivity is being continuously created throughout the course of a poem involving either a classical crescendo or a descent of affective intensity toward the end. In addition, this finding complements well with the established idea that readers naturally exert their greatest reading emphasis at the end of a sentence or passage Gopen and Swan, Last but not least, our data corroborate and extend recent findings on how sublexical phonological features influence affective processes during poetry reading.

That is, for instance, valence ratings of poems get more negative, or spitefulness ratings increase, when poems feature particularly many phonological segments of high arousal potential i. In the present study, using a huge number of predictors from different text levels, we could show that these effects of basic affective tone indeed seem to occur independently of the lexical affective content of the poems, as effects persist even after the very robust effects of lexical affective values have been partialled out in our multiple regression models.

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Note also that control measures of the basic affective tone —not using the phonologically salient but all phonological segments—only rarely account for significant amounts of variance of the ratings in our multiple regression models and if then only referring to specific subsyllabic units , while the EMOPHON's measures based on phonological salience did so in eight out of nine regression models. This is strong evidence that phonological salience in combination with phonological iconicity can be considered an important poetic device. Importantly, our data also show that readers are obviously sensitive to phonological salience per se: Subjective ratings of poeticity and onomatopoeia were significantly associated to the number of phonological segments qualified as phonologically salient by the EMOPHON tool.

At the level of rating dimensions as dependent variables—and from a general perspective—our study offers an interesting comparison between rather global evaluations of the general affective meaning of poems using the terms of dimensional emotion models valence and arousal , specific affective dimensions presumably best suitable for the given corpus sadness, spitefulness and friendliness , and the more aesthetic evaluations of liking and poeticity, as well as the further evaluation of onomatopoeia.

Goodness of fit for regression models trying to predict the latter three dimensions was clearly less as compared to the other two groups. This is no surprise, as in the case of valence and arousal ratings, criteria and predictor variables are based on identical operationalizations of affect as all predictors were quantified using valence and arousal values.

The author-given labels of spitefulness, sadness, and friendliness deliver even more impressive fits, presumably because they might simply capture the entire variance of affective content of these poems in optimal ways. Still, our approach offers interesting insights on how more abstract evaluations of poetry such as participants' liking of a poem or the ascription of poeticity and onomatopoeia to a text relate to the basic affective dimensions of valence and arousal at lexical and sublexical textual levels: A remarkable finding is the decrease in general liking ratings of poems with increasing arousal—concerning both the words or concepts dealt with in a poem, and its phonological content also see Aryani et al.

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As the general arousal level of the poems in the Enzensberger volume is on average very high, a lowered lexical arousal level, as indicated by the regression results for liking, would still be of medium value. This principle also seems to generalize to the evaluation of poeticity by our participants: Both very high and very low levels of lexical arousal go along with lesser ascriptions of poeticity to the poems. Also at the sublexical level, a rather low arousal level coincides with higher poeticity ratings. A similar pattern is present for the explicit evaluation of phonological content during onomatopoeia ratings: Most interestingly, they also decreased with increasing spreads of lexical valence and arousal.

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Again, the focus—at least the conscious one—of our participants on formal features of poetry appeared to be rather disturbed by a too distracting affective variety at the level of semantic content. Taken together, while previous studies had reported a range of effects of specific text levels influencing the affective appeal of literature e.

What we consider a characteristic strength of the current approach certainly represents a shortcoming when it comes to deliver a comprehensive model of poetry perception: While this alternative approach interestingly matches current computer-based approaches to poetic writing Kirke and Miranda, ; Misztal and Indurkhya, , it does not take into account well established phenomena of, e.

Furthermore, people less experienced with poetry might be less aware of more sophisticated stylistic devices or further meanings on a meta-level. Hence, basic textual features may play a bigger role in forming the general affective meaning of poetry for lay people than for experienced poetry readers. It would be interesting to investigate through follow-up studies with expert poetry consumers whether the influence of basic textual levels on affective perception would decrease with expertise. Further, also the choice of textual measures could still be extended—for example, integrating morphemic and syntactic text levels—and refined—for example in terms of the inter-lexical measures.

The merit of this study might thus just lie in having made first explorative steps toward investigating—or having opened initial insights on—text-based affective potential functions for several aspects of the general affective meaning. These innovative insights may also compensate methodological disadvantages of our statistical approach using a large number of predictors in stepwise multiple regression.

While we opted for this specific method as it seems optimal when screening for the most influential ones among a wide range of possible candidate measures, future studies may apply more fine grained methods to disentangle the details concerning the interplay of a restricted number of variables according to more specific research questions. Future studies should, in particular, extend our investigations to i the works of other writers—as some of our findings may in theory result from an idiosyncratic writing style of H. Enzensberger, ii non- literary texts or even everyday speech—in different languages, and iii affective ratings from different types of reader groups including expert readers.

In this study we focused on how and to which extent affective connotations of very basic textual measures at the lexical, inter-lexical, and even sublexical level of a poem—that can all be derived from existing normative databases—determine the perception of the general affective meaning of poetry in a way that proves quantifiable beyond the specific context of a given poem, author, or recipient. By applying an exhaustive exploratory regression analysis to a comprehensive corpus of poems and their ratings from hundreds of readers, we found that a significant amount of variance in discrete and dimensional affective ratings of poetry can be accounted for solely by text-based affective measures from different levels of processing.

In all of the presented statistical models—focusing on different aspects of the general affective meaning —variance of each rating dimension is significantly accounted for by affective properties of several text levels: Thus, our research brings together previous accounts on specific effects of single text levels, showing how they may co-exist each in their own right or interact to constitute the complex holistic framework of poetry perception.

SU collected and analyzed the data, as well as wrote the main part of the manuscript. AA was responsible for some of the computational aspects, especially regarding the Emophon tool developed by him, and offered important critical feedback. MK initiated the idea to use the poem corpus and gave helpful input from her philological perspective throughout the whole research process. AJ gave important input regarding the theoretical framework of the study. All authors substantially contributed to the conception and interpretation of the work, revised it critically, and agree to be accountable for all aspects of the work.

We conducted a non-experimental, voluntary online survey, in which people had to read and judge poems. In the instructions we told the participants that they can skip the survey any time they want to. If they had any questions regarding the survey they could contact us any time e-mail addresses provided. There was only one participant of the online survey who was only 17 years old. We did not have any additional instructions for minors or their parents.

But we assume that rating poetry does not pose a significant difference between teenagers and adults. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The Supplementary Material for this article can be found online at: Europe PMC requires Javascript to function effectively. Enzensberger had labeled as either "friendly," "sad," or "spiteful. The snippet could not be located in the article text. This may be because the snippet appears in a figure legend, contains special characters or spans different sections of the article.

Published online Jan Jacobs , 1, 2, 4 and Markus Conrad 1, 5. This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology. Received Feb 27; Accepted Dec The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.

No use, distribution or reproduction is permitted which does not comply with these terms.

This article has been cited by other articles in PMC. Abstract The literary genre of poetry is inherently related to the expression and elicitation of emotion via both content and form. Introduction Emotional impact constitutes an important aspect of poetry Turner and Poeppel, ; Cupchik, ; van Peer et al. Lexical effects on general affective meaning Lexical affective meaning has been shown to be of reliable predictive potential for the affective perception of different types of texts Anderson and McMaster, ; Whissell et al.

Sublexical effects on general affective meaning Poetry inherently involves the structuring of sound, which is why it is important to consider the phonological composition at the sublexical level—also and especially when investigating the emotional impact of poetry. Participants German native speakers were recruited through a post on the institute's website and a diversity of Facebook webpages. Procedure and variables General affective meaning ratings were acquired via an online survey using the QuestBack Unipark software.

A minimum of 15 complete ratings for each poem were acquired on each of the following eight dimensions—presented to participants in randomized order: Ratings of Valence and Arousal—Linking our Approach to Psychological Emotion Models — Valence 1 on a 7-point scale ranging from—3 very negative via 0 neutral to 3 positive.

Multiple regression The rating variables including the absolute value of valence were used as dependent variables in a multiple regression approach. Lexical predictors All poem texts were PoS part-of-speech tagged to identify the word forms and infinitives of each word.

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As example, the formula for the sigma factor of lexical arousal looks as follows: Inter-lexical predictors The inter-lexical variables that we included in the analysis are thought to reflect tensions and dynamics within a text. Standard deviations and spans of all words' valence and arousal values may serve as a proxy for the general affective spread of a poem: For the number of salient phonological segments that exceed their confidence intervals, we used the — N s of salient onsets, N s of salient nuclei, N s of salient codas as well as the N s of all salient subsyllabic segments altogether in each case weighted by the length of the respective poem.

Table 1 Means M and standard deviations SD of the rating variables for each author-given affective category N being the number of poems rated. Open in a separate window. Table 2 Bivariate correlation coefficients between all rating variables. Table 3 Predictors of the basic affective dimensions' ratings. The bold number indicates the respective overall cumulative R 2 corrected for each regression model. Table 4 Predictors of the discrete affective concepts' ratings.

Table 5 Predictors of the two aesthetic and the onomatopoeia ratings. SD, standard deviation, … , absolute value; Indicators of significance: Discussion This study investigates to which extent affective connotations at the rather basic textual dimensions of phonological units and single words or the relative positions of the latter influence the overall affective perception of poetry. Limitations and future prospects What we consider a characteristic strength of the current approach certainly represents a shortcoming when it comes to deliver a comprehensive model of poetry perception: Conclusion In this study we focused on how and to which extent affective connotations of very basic textual measures at the lexical, inter-lexical, and even sublexical level of a poem—that can all be derived from existing normative databases—determine the perception of the general affective meaning of poetry in a way that proves quantifiable beyond the specific context of a given poem, author, or recipient.

Conflict of interest statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Supplementary material The Supplementary Material for this article can be found online at: Computer assisted modeling of affective tone in written documents.

Extracting salient sublexical units from written texts: Measuring basic affective tone in poems via phonological saliency and iconicity. Arts 10 , — P is for Happiness, N is for Sadness: Can emotional valence in stories be determined from words? Johns Hopkins University Press. When we like what we know - A parametric fMRI analysis of beauty and familiarity. Instruction Manual and Affective Ratings.

The word frequency effect: The time course of emotion effects in first and second language processing: Poetics 23 , — The Perm State Institute of Culture; , 65— Cortical tracking of hierarchical linguistic structures in connected speech. Vorschule Der Asthetik, Vol. Word 17 , — Aesthetic emotions and reality. The science of scientific writing. The effects of educational background on literary and poetic text categorization judgements , in The Empirical Study of Literature , ed Rusch G. Integration of phonetic and graphic features in poetic text categorization judgements.

Poetic text processing and its empirical investigation. Poetics 16 , 75— Fiction feelings in Harry Potter: Neuroreport 25 , — The Magical activation of left amygdala when reading Harry Potter: An fMRI study on how descriptions of supra-natural events entertain and enchant. The emotion potential of words and passages in reading Harry Potter: Can Harry Potter still put a spell on us in a second language? An fMRI study on reading emotion-laden literature in late bilinguals.

Cortex 63 , — Elemente eines Modells des literarischen Lesens [Neurocognitive poetics: Wie wir unsere Wirklichkeiten konstruieren [Brain and Poetry: How we Construct our Realities] , eds Schrott R. Cambridge University Press; , — Bausteine einer Neurokognitiven Poetik: Mohr Siebeck; , 63— Linguistics and poetics , in Style in Language , ed Sebeok T.

The Framework of Language. Subliminal Verbal Patterning in Poetry. Poetics Today 2 , — Incidental effects of emotional valence in single word processing: NeuroImage 28 , — A model of aesthetic appreciation and aesthetic judgments. How art is appreciated. Emotional Experiences of Tension and Suspense: Psychological Mechanisms and Neural Correlates.

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