Sentiment Analysis for Words and Fiction Characters From The Perspective of Computational (Neuro-)Poetics

Two computational studies provide different sentiment analyses for text segments (e.g., ‘fearful’ passages) and figures (e.g., ‘Voldemort’) from the Harry Potter books (Rowling, 1997 - 2007) based on a novel simple tool called SentiArt. The tool uses vector space models together with theory-guided, empirically validated label lists to compute the valence of each word in a text by locating its position in a 2d emotion potential space spanned by the > 2 million words of the vector space model. After testing the tool’s accuracy with empirical data from a neurocognitive study, it was applied to compute emotional figure profiles and personality figure profiles (inspired by the so-called ‚big five’ personality theory) for main characters from the book series. The results of comparative analyses using different machine-learning classifiers (e.g., AdaBoost, Neural Net) show that SentiArt performs very well in predicting the emotion potential of text passages. It also produces plausible predictions regarding the emotional and personality profile of fiction characters which are correctly identified on the basis of eight character features, and it achieves a good cross-validation accuracy in classifying 100 figures into ‘good’ vs. ‘bad’ ones. The results are discussed with regard to potential applications of SentiArt in digital literary, applied reading and neurocognitive poetics studies such as the quantification of the hybrid hero potential of figures

Combining quantitative narrative analysis and predictive modeling - an eye tracking study

As a part of a larger interdisciplinary project on Shakespeare sonnets’ reception (Jacobs et al., 2017; Xue et al., 2017), 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 (cf. Jacobs, 2015c)

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

The Gutenberg English Poetry Corpus: Exemplary Quantitative Narrative Analyses

This paper describes a corpus of about 3,000 English literary texts with about 250 million words extracted from the Gutenberg project that span a range of genres from both fiction and non-fiction written by more than 130 authors (e.g., Darwin, Dickens, Shakespeare). Quantitative narrative analysis (QNA) is used to explore a cleaned subcorpus, the Gutenberg English Poetry Corpus (GEPC), which comprises over 100 poetic texts with around two million words from about 50 authors (e.g., Keats, Joyce, Wordsworth). Some exemplary QNA studies show author similarities based on latent semantic analysis, significant topics for each author or various text-analytic metrics for George Eliot’s poem “How Lisa Loved the King” and James Joyce’s “Chamber Music,” concerning, e.g., lexical diversity or sentiment analysis. The GEPC is particularly suited for research in Digital Humanities, Computational Stylistics, or Neurocognitive Poetics, e.g., as training and test corpus for stimulus development and control in empirical studies

The Foregrounding Assessment Matrix: An interface for qualitative-quantitative interdisciplinary research

This paper presents the results of a transdisciplinary research conducted by scholars working in the humanities and experimental psychologists in order to find an interface between the needs of a qualitative approach (mainly based on the evaluation of stylistic features) and those of a quantitative analysis, in order to find useful features for testing different reading behaviors and for new hermeneutical enquiries. The results of our research, which was conducted in two Labs (Dahlem Institute for Neuroimaging of Emotion at the FU Berlin and the NewHums – Neurocognitive and Human Studies at the University of Catania), consistently differ from previous ones, as they focus on the whole multi-layered foregrounded texture of a poem and try to evaluate predictable differences in reading, re-reading behaviour and meaning-making processes. We present the FAM, targeting foregrounding elements in three main categories: the phonological, morpho-syntactic, and rhetoric. To identify those elements, four different text levels were taken into account, the sublexical level of phonemes and syllables, the lexical level of single words, the interlexical level of word combinations across longer distance (e.g. two lines), and the supralexical level of whole stanzas or an entire poem. In contrast to previous quantitative analyses on short, isolated sentences and texts, mostly expository in nature (‘textoids’), or on single words or segments, the text is considered as a whole, marked by density fields that work as milestones along a reading route.This paper presents the results of a transdisciplinary research conducted by scholars working in the humanities and experimental psychologists in order to find an interface between the needs of a qualitative approach (mainly based on the evaluation of stylistic features) and those of a quantitative analysis, in order to find useful features for testing different reading behaviors and for new hermeneutical enquiries. The results of our research, which was conducted in two Labs (Dahlem Institute for Neuroimaging of Emotion at the FU Berlin and the NewHums – Neurocognitive and Human Studies at the University of Catania), consistently differ from previous ones, as they focus on the whole multi-layered foregrounded texture of a poem and try to evaluate predictable differences in reading, re-reading behaviour and meaning-making processes. We present the FAM, targeting foregrounding elements in three main categories: the phonological, morpho-syntactic, and rhetoric. To identify those elements, four different text levels were taken into account, the sublexical level of phonemes and syllables, the lexical level of single words, the interlexical level of word combinations across longer distance (e.g. two lines), and the supralexical level of whole stanzas or an entire poem. In contrast to previous quantitative analyses on short, isolated sentences and texts, mostly expository in nature (‘textoids’), or on single words or segments, the text is considered as a whole, marked by density fields that work as milestones along a reading route.This paper presents the results of a transdisciplinary research conducted by scholars working in the humanities and experimental psychologists in order to find an interface between the needs of a qualitative approach (mainly based on the evaluation of stylistic features) and those of a quantitative analysis, in order to find useful features for testing different reading behaviors and for new hermeneutical enquiries. The results of our research, which was conducted in two Labs (Dahlem Institute for Neuroimaging of Emotion at the FU Berlin and the NewHums – Neurocognitive and Human Studies at the University of Catania), consistently differ from previous ones, as they focus on the whole multi-layered foregrounded texture of a poem and try to evaluate predictable differences in reading, re-reading behaviour and meaning-making processes. We present the FAM, targeting foregrounding elements in three main categories: the phonological, morpho-syntactic, and rhetoric. To identify those elements, four different text levels were taken into account, the sublexical level of phonemes and syllables, the lexical level of single words, the interlexical level of word combinations across longer distance (e.g. two lines), and the supralexical level of whole stanzas or an entire poem. In contrast to previous quantitative analyses on short, isolated sentences and texts, mostly expository in nature (‘textoids’), or on single words or segments, the text is considered as a whole, marked by density fields that work as milestones along a reading route.This paper presents the results of a transdisciplinary research conducted by scholars working in the humanities and experimental psychologists in order to find an interface between the needs of a qualitative approach (mainly based on the evaluation of stylistic features) and those of a quantitative analysis, in order to find useful features for testing different reading behaviors and for new hermeneutical enquiries. The results of our research, which was conducted in two Labs (Dahlem Institute for Neuroimaging of Emotion at the FU Berlin and the NewHums – Neurocognitive and Human Studies at the University of Catania), consistently differ from previous ones, as they focus on the whole multi-layered foregrounded texture of a poem and try to evaluate predictable differences in reading, re-reading behaviour and meaning-making processes. We present the FAM, targeting foregrounding elements in three main categories: the phonological, morpho-syntactic, and rhetoric. To identify those elements, four different text levels were taken into account, the sublexical level of phonemes and syllables, the lexical level of single words, the interlexical level of word combinations across longer distance (e.g. two lines), and the supralexical level of whole stanzas or an entire poem. In contrast to previous quantitative analyses on short, isolated sentences and texts, mostly expository in nature (‘textoids’), or on single words or segments, the text is considered as a whole, marked by density fields that work as milestones along a reading route.This paper presents the results of a transdisciplinary research conducted by scholars working in the humanities and experimental psychologists in order to find an interface between the needs of a qualitative approach (mainly based on the evaluation of stylistic features) and those of a quantitative analysis, in order to find useful features for testing different reading behaviors and for new hermeneutical enquiries. The results of our research, which was conducted in two Labs (Dahlem Institute for Neuroimaging of Emotion at the FU Berlin and the NewHums – Neurocognitive and Human Studies at the University of Catania), consistently differ from previous ones, as they focus on the whole multi-layered foregrounded texture of a poem and try to evaluate predictable differences in reading, re-reading behaviour and meaning-making processes. We present the FAM, targeting foregrounding elements in three main categories: the phonological, morpho-syntactic, and rhetoric. To identify those elements, four different text levels were taken into account, the sublexical level of phonemes and syllables, the lexical level of single words, the interlexical level of word combinations across longer distance (e.g. two lines), and the supralexical level of whole stanzas or an entire poem. In contrast to previous quantitative analyses on short, isolated sentences and texts, mostly expository in nature (‘textoids’), or on single words or segments, the text is considered as a whole, marked by density fields that work as milestones along a reading route

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

To Like Or Not to Like, That Is the Question

Perhaps the most ubiquitous and basic affective decision of daily life is deciding whether we like or dislike something/somebody, or, in terms of psychological emotion theories, whether the object/subject has positive or negative valence. Indeed, people constantly make such liking decisions within a glimpse and, importantly, often without expecting any obvious benefit or knowing the exact reasons for their judgment. In this paper, we review research on such elementary affective decisions (EADs) that entail no direct overt reward with a special focus on Neurocognitive Poetics and discuss methods and models for investigating the neuronal and cognitive-affective bases of EADs to verbal materials with differing degrees of complexity. In line with evolutionary and appraisal theories of (aesthetic) emotions and data from recent neurocognitive studies, the results of a decision tree modeling approach simulating EADs to single words suggest that a main driving force behind EADs is the extent to which such high-dimensional stimuli are associated with the “basic” emotions joy/happiness and disgust

A neurocognitive poetics investigation of eye movements during the reading of Baudelaire’s ‘Les Chats’

Following Jakobson and Levi-Strauss famous analysis of Baudelaire’s poem ‘Les Chats’ (‘The Cats’), in the present study we investigated the reading of French poetry from a Neurocognitive Poetics perspective. Our study is exploratory and a first attempt in French, most previous work having been done in either German or English (e.g., Jacobs, 2015a, 2018a, b; Müller et al., 2017; Xue et al., 2019). We varied the presentation mode of the poem Les Chats (verse vs. prose form) and measured the eye movements of our readers to test the hypothesis of an interaction between presentation mode and reading behavior. We specifically focussed on rhyme scheme effects on standard eye movement parameters. Our results replicate those from previous English poetry studies in that there is a specific pattern in poetry reading with longer gaze durations and more rereading in the verse than in the prose format. Moreover, presentation mode also matters for making salient the rhyme scheme. This first study generates interesting hypotheses for further research applying quantitative narrative analysis to French poetry and developing the Neurocognitive Poetics Model of literary reading (NCPM; Jacobs, 2015a) into a cross-linguistic model of poetry reading

Rereading Shakespeare’s Sonnets — an Eye Tracking Study

Texts are often reread in everyday life, but most studies of rereading have been based on expository texts, not on literary ones such as poems, though literary texts may be reread more often than others. To correct this bias, the present study is based on two of Shakespeare’s sonnets. Eye movements were recorded, as participants read a sonnet then read it again after a few minutes. After each reading, comprehension and appreciation were measured with the help of a questionnaire. In general, compared to the first reading, rereading improved the fluency of reading (shorter total reading times, shorter regression times, and lower fixation probability) and the depth of comprehension. Contrary to the other rereading studies using literary texts, no increase in appreciation was apparent. Moreover, results from a predictive modeling analysis showed that readers’ eye movements were determined by the same psycholinguistic features throughout the two sessions. Apparently, even in the case of poetry, the process of reading is determined mainly by surface features of the text, unaffected by repetition