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

An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing

We present results from a project on sentiment analysis of drama texts, more concretely the plays of Gotthold Ephraim Lessing. We conducted an annotation study to create a gold standard for a systematic evaluation. The gold standard consists of 200 speeches of Lessing’s plays and was manually annotated with sentiment information by five annotators. We use the gold standard data to evaluate the performance of different German sentiment lexicons and processing configurations like lemmatization, the extension of lexicons with historical linguistic variants, and stop words elimination, to explore the influence of these parameters and to find best practices for our domain of application. The best performing configuration accomplishes an accuracy of 70%. We discuss the problems and challenges for sentiment analysis in this area and describe our next steps toward further research

Generating High-Quality Emotion Arcs For Low-Resource Languages Using Emotion Lexicons

Automatically generated emotion arcs -- that capture how an individual or a population feels over time -- are widely used in industry and research. However, there is little work on evaluating the generated arcs in English (where the emotion resources are available) and no work on generating or evaluating emotion arcs for low-resource languages. Work on generating emotion arcs in low-resource languages such as those indigenous to Africa, the Americas, and Australia is stymied by the lack of emotion-labeled resources and large language models for those languages. Work on evaluating emotion arcs (for any language) is scarce because of the difficulty of establishing the true (gold) emotion arc. Our work, for the first time, systematically and quantitatively evaluates automatically generated emotion arcs. We also compare two common ways of generating emotion arcs: Machine-Learning (ML) models and Lexicon-Only (LexO) methods. By running experiments on 42 diverse datasets in 9 languages, we show that despite being markedly poor at instance level emotion classification, LexO methods are highly accurate at generating emotion arcs when aggregating information from hundreds of instances. (Predicted arcs have correlations ranging from 0.94 to 0.99 with the gold arcs for various emotions.) We also show that for languages with no emotion lexicons, automatic translations of English emotion lexicons can be used to generate high-quality emotion arcs -- correlations above 0.9 with the gold emotion arcs in all six indigenous African languages explored. This opens up avenues for work on emotions in numerous languages from around the world; crucial not only for commerce, public policy, and health research in service of speakers of those languages, but also to draw meaningful conclusions in emotion-pertinent research using information from around the world (thereby avoiding a western-centric bias in research).Comment: 32 pages, 16 figures. arXiv admin note: substantial text overlap with arXiv:2210.0738

Una nueva visión de la supuesta influencia de Madame Bovary en La Regenta a través de la estilometría y el análisis de sentimientos basados en lenguaje R

Madame Bovary's supposed influence on La Regenta has been the subject of numerous critical studies although, since the beginning, it has been surrounded by controversy and debate. The traditionally adopted approach has been qualitative and based on partial, and not always objective, data. Furthermore, only merely anecdotal impressions have been sometimes the basis of different hypotheses and, consequently, the results obtained have been discordant. The main goal of this work is to provide quantitative data that allow to answer this still open question. To this end, a computational analysis of both the stylistic patterns and the emotional dimension, which underlie both novels, will be carried out by using the programming language R. In addition, the comparison between the original version of Madame Bovary and its translation into Spanish will also be addressed to test a new model for identifying equivalence in translation. Despite its limitations due its novelty, this approach can be a first step to examine new ways for investigating phenomena such as assimilation, imitation, intertextuality or plagiarism in literary texts, as well as equivalence in translation.La supuesta influencia de Madame Bovary en La Regenta, rodeada desde el inicio de polémicas y enfrentamientos, ha sido objeto de numerosos estudios críticos. El enfoque tradicionalmente adoptado ha sido de tipo cualitativo y se ha fundado en datos parciales, no siempre objetivos. Es más, en ocasiones, se han tomado como base de las distintas hipótesis tan solo impresiones meramente anecdóticas y, en consecuencia, los resultados obtenidos han sido discordantes. El objetivo principal de este trabajo es aportar datos cuantitativos que contribuyan a dar respuesta a esta cuestión aún abierta. Con este fin, llevaremos a cabo un análisis computacional de los patrones estilísticos y la dimensión emotiva que subyacen en ambas novelas utilizando para ello el lenguaje de programación R. Además de este objetivo primario se abordará también secundariamente la comparación de la versión original de Madame Bovary con su traducción al español, a fin de someter a experimentación un nuevo modelo de acercamiento a la equivalencia traductora. A pesar de que, dada su novedad, este enfoque presenta aún limitaciones, puede constituir un primer paso para explorar nuevas vías de investigación de fenómenos como la asimilación, la imitación, la intertextualidad o el plagio en textos literarios, así como de la equivalencia en traducción

Examining the representation of landscape and its emotional value in German-Swiss fiction between 1840 and 1940

This paper presents a quantitative analysis of the representation and affective encoding of fictional space in a corpus of 125 Swiss literary prose texts of the 19th and early 20th Century written in German, offering a contribution to both spatial and affective literary studies. Motivated by questions about the iconic dichotomy between ‘urban’ and ‘rural/natural’ space in literary works (Sengle; Fournier; Nell and Weiland) – and in Swiss literature around 1900 in particular (Rehm) – we use computational methods to detect and examine how different types of space are distributed and affectively encoded in German-Swiss literature. Taking into account the complexity of cultural perceptions and representations of space across history, we examine the presence of ‘urban’ and ‘rural/natural’ fictional spaces and their potential role in constructing a ‘Swiss’ national literature (Böhler; Zimmer), and their affective encoding. In order to do this, we first compiled a comprehensive dictionary of named and non-named spatial entities in the broad spatial categories RURAL and URBAN, and examined the presence of sentiment and emotions (valence and discrete emotions) and their ‘strength’ (arousal) in relation to these. We used current state-of-the-art sentiment lexicons for German available to the digital humanities community. Similarly to Heuser et al., we mapped the spatial entities and the sentiment lexicons onto our corpus, and focused on spans of +/-50 words around the detected entities, in order to examine the specific sentiment and emotions related to space. In an exploratory analysis, we offer here a first-time data-driven perspective on rural and urban fictional space, incorporating the dimension of affective encoding of space systematically

Rezensiv - Online-Rezensionen und Kulturelle Bildung

Online-Rezensionen zu künstlerischen Artefakten können Bildungsprozesse anstoßen. Sowohl in der produktiven Auseinandersetzung mit einem Werk als auch in der Aufbereitung dieser Erfahrung in einem rezensiven Text und für ein spezifisches Publikum liegt ein hohes Potenzial hinsichtlich der kulturellen Teilhabe und Überwindung von Bildungsbarrieren. Aber welche Prozesse, Inhalte und Kontexte spielen dabei eine Rolle? Dieser Frage widmete sich das interdisziplinäre Forschungsprojekt Rez@Kultur, dessen Ergebnisse hier erstmals umfassend dargestellt werden. Ergänzt werden die Befunde um Anschlussperspektiven und Kommentare aus Forschung und Praxis

Rezensiv - Online-Rezensionen und Kulturelle Bildung

Online-Rezensionen zu künstlerischen Artefakten können Bildungsprozesse anstoßen. Sowohl in der produktiven Auseinandersetzung mit einem Werk als auch in der Aufbereitung dieser Erfahrung in einem rezensiven Text und für ein spezifisches Publikum liegt ein hohes Potenzial hinsichtlich der kulturellen Teilhabe und Überwindung von Bildungsbarrieren. Aber welche Prozesse, Inhalte und Kontexte spielen dabei eine Rolle? Dieser Frage widmete sich das interdisziplinäre Forschungsprojekt Rez@Kultur, dessen Ergebnisse hier erstmals umfassend dargestellt werden. Ergänzt werden die Befunde um Anschlussperspektiven und Kommentare aus Forschung und Praxis

Predicting sentiments and space in Swiss literature using BERT and Prodigy

Grisot G, Pennino F, Herrmann JB. Predicting sentiments and space in Swiss literature using BERT and Prodigy. Presented at the 3rd Conference on Compuational Humanities Research, Antwerp.Thanks to the development of new powerful technologies for computational data analysis, an increasing number of researchers has investigated sentiment in texts, making use of traditional corpus linguistic approaches as well as machine learning tools. When considering literary texts, however, sentiment analysis is still in its infancy, especially when it focuses on languages other than English [1]. Crucially, only very few studies so far have related the representation of sentiment and emotions to that of space. This has depended partly on the limited amount of literary texts available digitally and partly of the challenges of defining and identifying space in literature. Emotions and space are however central to the experience of literary narrative [2, 3, 4], and recent advances in their systematic, quantitative analysis have been made within computational literary studies [5, 6, 7]. Using lexicon-based methods, Grisot and Herrmann [8] investigated emotions and sentiments in relation to the representation of literary space, looking in particular at the differences between the rural and urban landscapes portrayed in a corpus of Swiss novels written in German. The present paper takes a step forward, building on their data and using manual annotation and advanced machine learning methods to train a fine-tuned model, in order to automatically detect and recognise on the one hand sentiment (valence, arousal) and discrete emotions (joy, anger, sadness, disgust, fear, surprise), and on the other spatial entities (named and unnamed), in a historical corpus of Swiss novels. With such model, we aim at higher levels of lexical coverage and validity when compared to existing results obtained with sentiment lexicons and entities lists. Using a language model trained on a large corpus (3000+) of German literary texts spanning from 1800 to 1950 (Literary German BERT [9]), we make use of BERT word embeddings [10], Prodigy active learning tool [11] and manually annotated sentences to recognise sentiment, emotions and space, and see whether and how these relate to one another. More than 6000 sentences were taken from Swiss-German novels and annotated for discrete emotions, valence (understood here as the degree of 'positivity' of the detected emotion) and arousal (its 'intensity' or 'degree of activation'), while active learning was used on more than 4000 sentences to implement existing lists of labelled spatial entities. Annotations were conducted by several trained student assistants. The annotated samples were employed to train a deep learning classifier using BERT transformers. In this preliminary phase we reached an accuracy over 70% on valence prediction, an over 66% on emotion prediction, and an around 64% on arousal prediction. In terms of space, we used active learning on a word2vec model bootstrapped with a Swiss Geographic location corpus, annotating sentences on six categories (geolocations: *geo-rural, geo-natural, geo-urban*, and spatial terms (unnamed): *rural, natural, urban*). For these, we obtained the following preliminary results: F1: .65, precision: .66, and recall: .64. These scores are very promising, suggesting the possibility – provided more training data – of a full automation of the annotation task on our domain of historical literary texts, both in terms of sentiments and in terms of spatial entities. We are currently gathering more annotations, and at the time of the conference shall be able to update the results on a broader data base, and to show whether our model will be able to predict a relation between sentiments and space in Swiss literature. While potentially taking automatic SA of German literary texts to a new level, our study also allows evaluating the performance of lexicon-based in direct comparison with deep learning SA approaches, thus allowing to gauge the validity of different SA methods on a data-driven basis. This approach also raises questions concerning the effect of genre on the ease and validity of manual sentiment annotations. References [1] R. Klinger, S. S. Suliya, N. Reiter, Automatic Emotion Detection for Quantitative Literary Studies. A case study based on Franz Kafka's “Das Schloss” and “Amerika”, Proceedings of the Digital Humanities (2016). [2] K. Oatley, A taxonomy of the emotions of literary response and a theory of identification in fictional narrative, Poetics 23 (1995) 53–74. URL: https://www.sciencedirect.com/science/article/pii/0304422X94P4296S. doi:https://doi.org/10.1016/0304-422X(94) P4296-S. [3] K. Oatley, Fiction and its study as gateways to the mind, Scientific Study of Literature 1 (2011) 153–164. doi:10.1075/ssol.1.1.16oat. [4] P. C. Hogan, Affect Studies, 2016. URL: https://oxfordre.com/literature/view/10.1093/acrefore/9780190201098.001.0001/acrefore-9780190201098-e-105. doi:10.1093/acrefore/9780190201098.013.105. [5] R. Heuser, M. Algee-Hewitt, A. Lockhart, Mapping the emotions of London in fiction, 1700–1900: A crowdsourcing experiment, in: Literary mapping in the digital age, Routledge, 2016, pp. 43–64. [6] M. Jockers, Extracts sentiment and sentiment-derived plot arcs from text, R package “syuzhet (2017). [7] M. Burghardt, C. Wolff, T. Schmidt, Toward multimodal sentiment analysis of historic plays: A case study with text and audio for Lessing's Emilia Galotti, in: 4th Conference of the Association Digital Humanities in the Nordic Countries, Copenhagen, 2019. [8] G. Grisot, J. B. Herrmann, Examining the representation of landscape and its emotional value in German-Swiss fiction around 1900, 2022. [9] F. Fischer, J. Str ̈otgen, Corpus of German-Language Fiction (txt), 2017. URL: https://figshare.com/articles/dataset/Corpus of German-Language Fiction txt /4524680https://ndownloader.figshare.com/files/7320866. doi:10.6084/m9.figshare.4524680.v1. [10] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805 (2018). [11] I. Montani, M. Honnibal, Prodigy: A new annotation tool for radically efficient machine teaching, Artificial Intelligence to appear (2018)