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

Predicting subjective ratings of affect and comprehensibility with text features: a reader response study of narrative poetry

Literary reading is an interactive process between a reader and a text that depends on a balance between cognitive effort and emotional rewards. By studying both the crucial features of the text and of the subjective reader reception, a better understanding of this interactive process can be reached. In the present study, subjects (N=31) read and rated a work of narrative fiction that was written in a poetic style, thereby offering the readers two pathways to cognitive rewards: Aesthetic appreciation and narrative immersion. Using purely text-based quantitative descriptors, we were able to independently and accurately predict the subjective ratings in the dimensions comprehensibility, valence, arousal, and liking across roughly 140 pages of naturalistic text. The specific text features that were most important in predicting each rating dimension are discussed in detail. In addition, the implications of the findings are discussed more generally in the context of existing models of literary processing and future research avenues for empirical literary studies.

The role of valence and arousal for phonological iconicity in the lexicon of German: a cross-validation study using pseudoword ratings

The notion of sound symbolism receives increasing interest in psycholinguistics. Recent research - including empirical effects of affective phonological iconicity on language processing (Adelman et al., 2018; Conrad et al., 2022) - suggested language codes affective meaning at a basic phonological level using specific phonemes as sublexical markers of emotion. Here, in a series of 8 rating-experiments, we investigate the sensitivity of language users to assumed affectively-iconic systematic distribution patterns of phonemes across the German vocabulary:After computing sublexical-affective-values (SAV) concerning valence and arousal for the entire German phoneme inventory according to occurrences of syllabic onsets, nuclei and codas in a large-scale affective normative lexical database, we constructed pseudoword material differing in SAV to test for subjective affective impressions.Results support affective iconicity as affective ratings mirrored sound-to-meaning correspondences in the lexical database. Varying SAV of otherwise semantically meaningless pseudowords altered affective impressions: Higher arousal was consistently assigned to pseudowords made of syllabic constituents more often used in high-arousal words - contrasted by less straightforward effects of valence SAV. Further disentangling specific differential effects of the two highly-related affective dimensions valence and arousal, our data clearly suggest arousal, rather than valence, as the relevant dimension driving affective iconicity effects.

ERPs reveal an iconic relation between sublexical phonology and affective meaning

Classical linguistic theory assumes that formal aspects, like sound, are not internally related to the meaning of words. However, recent research suggests language might code affective meaning such as threat and alert sublexically. Positing affective phonological iconicity as a systematic organization principle of the German lexicon, we calculated sublexical affective values for sub-syllabic phonological word segments from a large-scale affective lexical German database by averaging valence and arousal ratings of all words any phonological segment appears in. We tested word stimuli with either consistent or inconsistent mappings between lexical affective meaning and sublexical affective values (negative-valence/high-arousal vs. neutral-valence/low-arousal) in an EEG visual-lexical-decision task. A mismatch between sublexical and lexical affective values elicited an increased N400 response. These results reveal that systematic affective phonological iconicity - extracted from the lexicon - impacts the extraction of lexical word meaning during reading.

DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels

Nowadays, there are many applications of text mining over corpora from different languages. However, most of them are based on texts in prose, lacking applications that work with poetry texts. An example of an application of text mining in poetry is the usage of features derived from their individual words in order to capture the lexical, sublexical and interlexical meaning, and infer the General Affective Meaning (GAM) of the text. However, even though this proposal has been proved as useful for poetry in some languages, there is a lack of studies for both Spanish poetry and for highly-structured poetic compositions such as sonnets. This article presents a study over an annotated corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words for predicting their GAM. The purpose of this is to model sonnets at an affective level. The article also analyses the relationship between the GAM of the sonnets and the content itself. For this, we consider the content from a psychological perspective, identifying with tags when a sonnet is related to a specific term. Then, we study how GAM changes according to each of those psychological terms. The corpus used contains 274 Spanish sonnets from authors of different centuries, from fifteenth to nineteenth. This corpus was annotated by different domain experts. The experts annotated the poems with affective and lexico-semantic features, as well as with domain concepts that belong to psychology. Thanks to this, the corpus of sonnets can be used in different applications, such as poetry recommender systems, personality text mining studies of the authors, or the usage of poetry for therapeutic purposes.

Emotional Valence Coded in the Phonemic Content – Statistical Evidence Based on Corpus Analysis

This study investigates the relationship between the phonemic content of texts in English and the emotional valence they inspire. The sublexical content is presented in terms of biphones composed by one vowel and one consonant. The statistical analysis of a vast corpus of emotionally evaluated sentences reveals a strong correlation between this sublexical presentation and the evaluations of valence provided by the readers. An initial test performed with other valence-rated prose texts makes believing that the feature observed within the corpus can be useful for the emotion classification of texts.

Computing the Affective-Aesthetic Potential of Literary Texts

In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (>150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSM’s ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 > 0.5, p < 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p < 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results.

Is less readable liked better? The case of font readability in poetry appreciation

Previous research shows conflicting findings for the effect of font readability on comprehension and memory for language. It has been found that—perhaps counterintuitively–a hard to read font can be beneficial for language comprehension, especially for difficult language. Here we test how font readability influences the subjective experience of poetry reading. In three experiments we tested the influence of poem difficulty and font readability on the subjective experience of poems. We specifically predicted that font readability would have opposite effects on the subjective experience of easy versus difficult poems. Participants read poems which could be more or less difficult in terms of conceptual or structural aspects, and which were presented in a font that was either easy or more difficult to read. Participants read existing poems and subsequently rated their subjective experience (measured through four dependent variables: overall liking, perceived flow of the poem, perceived topic clarity, and perceived structure). In line with previous literature we observed a Poem Difficulty x Font Readability interaction effect for subjective measures of poetry reading. We found that participants rated easy poems as nicer when presented in an easy to read font, as compared to when presented in a hard to read font. Despite the presence of the interaction effect, we did not observe the predicted opposite effect for more difficult poems. We conclude that font readability can influence reading of easy and more difficult poems differentially, with strongest effects for easy poems.

The Impact of Context on Affective Norms: A Case of Study With Suspense

The emotional response to a stimulus is typically measured in three variables called valence, arousal and dominance. Based on such dimensions, Bradley and Lang (1999) published the Affective Norms for English Words (ANEW), a corpus of affective ratings for 1,034 non-contextualized words. Expanded and adapted to many languages, ANEW provides a corpus to evaluate and to predict human responses to different stimuli, and it has been used in a number of studies involving analysis of emotions. However, ANEW seems not to appropriately predict affective responses to concepts when these are contextualized in certain situational backgrounds, in which words can have different connotations from those in non-contextualized scenarios. These contextualized affective norms have not been sufficiently contrasted yet because the literature does not provide a corpus of the ANEW list in specific contexts. On this basis, this paper reports on the creation of a new corpus of affective norms for the original 1,034 ANEW words in a particular context (a fictional scene of suspense). An extensive quantitative data analysis comparing both corpora was carried out, confirming that the affective ratings are highly influenced by the context. The corpus can be downloaded as Supplementary Material.

Towards emotion recognition in texts: A sound-symbolic experiment

The purpose of this study is to investigate the relationship between the phonetic content of prose texts in English and the emotion that the texts inspire, namely - the effect of vowel-consonant bi-phones on subjects’ evaluation of positive or negative emotional valence when reading. The methodology is based on data from an experiment where the participants, native speakers of three different languages, evaluated the valence invoked in them by one-page texts from English books. The sub-lexical level of the texts was obtained using phonetic transcriptions of the words and their further decomposition into vowel-consonant bi-phones. The statistical investigation relies on density-measures of the investigated bi-phones over each text as a whole. The result shows that there exists a correlation between the obtained sub-lexical representation and the valence perceived by the readers. Concerning the type of the consonants in the bi-phones (abrupt or sonorant), the influence of the abrupt bi-phones is stronger. However, sub-sets of both types of bi-phones showed relatedness with the emotional valence conveyed by the texts. In conclusion, the speech, expressed in written form, is laden with emotional valence even when the words’ lexicological meaning is not taken into consideration and the words are apprehended as mere phonetic constructs. This prompts hypothesizing that words’ semantics itself is partly underpinned by some mental emotion-related level of conceptualization, influenced by sounds. For practical purposes, the result suggests that based on the syllabic content of a text it should be possible to predict the valence that the text would inspire in its readers.

Reading Shakespeare Sonnets: Combining Quantitative Narrative Analysis and Predictive Modeling -an Eye Tracking Study

As a part of a larger interdisciplinary project on Shakespeare sonnets' reception (1, 2), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning-based predictive modeling approach five 'surface' features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. The fact that one phonological feature, i.e., sonority score, also played a role is in line with current theorizing on poetry reading. Our approach opens new ways for future eye movement research on reading poetic texts and other complex literary materials(3).

Why 'piss' is ruder than 'pee'? The role of sound in affective meaning making

Most language users agree that some words sound harsh (e.g. grotesque) whereas others sound soft and pleasing (e.g. lagoon). While this prominent feature of human language has always been creatively deployed in art and poetry, it is still largely unknown whether the sound of a word in itself makes any contribution to the word's meaning as perceived and interpreted by the listener. In a large-scale lexicon analysis, we focused on the affective substrates of words' meaning (i.e. affective meaning) and words' sound (i.e. affective sound); both being measured on a two-dimensional space of valence (ranging from pleasant to unpleasant) and arousal (ranging from calm to excited). We tested the hypothesis that the sound of a word possesses affective iconic characteristics that can implicitly influence listeners when evaluating the affective meaning of that word. The results show that a significant portion of the variance in affective meaning ratings of printed words depends on a number of spectral and temporal acoustic features extracted from these words after converting them to their spoken form (study1). In order to test the affective nature of this effect, we independently assessed the affective sound of these words using two different methods: through direct rating (study2a), and through acoustic models that we implemented based on pseudoword materials (study2b). In line with our hypothesis, the estimated contribution of words' sound to ratings of words' affective meaning was indeed associated with the affective sound of these words; with a stronger effect for arousal than for valence. Further analyses revealed crucial phonetic features potentially causing the effect of sound on meaning: For instance, words with short vowels, voiceless consonants, and hissing sibilants (as in 'piss') feel more arousing and negative. Our findings suggest that the process of meaning making is not solely determined by arbitrary mappings between formal aspects of words and concepts they refer to. Rather, even in silent reading, words' acoustic profiles provide affective perceptual cues that language users may implicitly use to construct words' overall meaning.

The Fictive Brain: Neurocognitive Correlates of Engagement in Literature

Fiction is vital to our being. Many people enjoy engaging with fiction every day. Here we focus on literary reading as 1 instance of fiction consumption from a cognitive neuroscience perspective. The brain processes which play a role in the mental construction of fiction worlds and the related engagement with fictional characters, remain largely unknown. The authors discuss the neurocognitive poetics model ( Jacobs, 2015a ) of literary reading specifying the likely neuronal correlates of several key processes in literary reading, namely inference and situation model building, immersion, mental simulation and imagery, figurative language and style, and the issue of distinguishing fact from fiction. An overview of recent work on these key processes is followed by a discussion of methodological challenges in studying the brain bases of fiction processing.

Affective Congruence between Sound and Meaning of Words Facilitates Semantic Decision

A similarity between the form and meaning of a word (i.e., iconicity) may help language users to more readily access its meaning through direct form-meaning mapping. Previous work has supported this view by providing empirical evidence for this facilitatory effect in sign language, as well as for onomatopoetic words (e.g., cuckoo) and ideophones (e.g., zigzag). Thus, it remains largely unknown whether the beneficial role of iconicity in making semantic decisions can be considered a general feature in spoken language applying also to “ordinary” words in the lexicon. By capitalizing on the affective domain, and in particular arousal, we organized words in two distinctive groups of iconic vs. non-iconic based on the congruence vs. incongruence of their lexical (meaning) and sublexical (sound) arousal. In a two-alternative forced choice task, we asked participants to evaluate the arousal of printed words that were lexically either high or low arousing. In line with our hypothesis, iconic words were evaluated more quickly and more accurately than their non-iconic counterparts. These results indicate a processing advantage for iconic words, suggesting that language users are sensitive to sound-meaning mappings even when words are presented visually and read silently.

Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective

In this paper I would like to pave the ground for future studies in Computational Stylistics and (Neuro-)Cognitive Poetics by describing procedures for predicting the subjective beauty of words. A set of eight tentative word features is computed via Quantitative Narrative Analysis (QNA) and a novel metric for quantifying word beauty, the aesthetic potential is proposed. Application of machine learning algorithms fed with this QNA data shows that a classifier of the decision tree family excellently learns to split words into beautiful vs. ugly ones. The results shed light on surface and semantic features theoretically relevant for affective-aesthetic processes in literary reading and generate quantitative predictions for neuroaesthetic studies of verbal materials.