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Grisot G, Pennino F, Herrmann JB. Predicting sentiments and space in Swiss literature using BERT and Prodigy. Presented at the CHR2023 - 3rd Conference on Computational 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 ...
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