Affect as a proxy for literary mood

Öhman, Emily; Rossi, Riikka

doi:10.46298/jdmdh.11164

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…However, it has been argued that more real-world congruent results with reusable methods can be obtained by using lexicons either independently (Teodorescu & Mohammad, 2022) or together with data-driven methods (Öhman, 2021). Öhman and Rossi (2023) use emotion lexicons to create affective word embeddings that allow them to create domainspecific models that take semantic shifts into account when attempting to use affect as a proxy for mood in literary texts.…”

Section: Computational Approaches To Shame and Guilt Detectionmentioning

confidence: 99%

Contrasting the semantic space of ‘shame’ and ‘guilt’ in English and Japanese

Diegoli,

Öhman

2024

Lang. cogn.

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This article sheds light on the significant yet nuanced roles of shame and guilt in influencing moral behaviour, a phenomenon that became particularly prominent during the COVID-19 pandemic with the community’s heightened desire to be seen as moral. These emotions are central to human interactions, and the question of how they are conveyed linguistically is a vast and important one. Our study contributes to this area by analysing the discourses around shame and guilt in English and Japanese online forums, focusing on the terms shame, guilt, haji (‘shame’) and zaiakukan (‘guilt’). We utilise a mix of corpus-based methods and natural language processing tools, including word embeddings, to examine the contexts of these emotion terms and identify semantically similar expressions. Our findings indicate both overlaps and distinct differences in the semantic landscapes of shame and guilt within and across the two languages, highlighting nuanced ways in which these emotions are expressed and distinguished. This investigation provides insights into the complex dynamics between emotion words and the internal states they denote, suggesting avenues for further research in this linguistically rich area.

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Section: Computational Approaches To Shame and Guilt Detectionmentioning

confidence: 99%