Affect as a proxy for literary mood
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Abstract (arXiv)
We propose to use affect as a proxy for mood in literary texts. In this study, we explore the differences in computationally detecting tone versus detecting mood. Methodologically we utilize affective word embeddings to look at the affective distribution in different text segments. We also present a simple yet efficient and effective method of enhancing emotion lexicons to take both semantic shift and the domain of the text into account producing real-world congruent results closely matching both contemporary and modern qualitative analyses.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 57 references that are in the map. First posted on arXiv 6 Apr 2023.
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14 of 57 references.
References · 14
- 2013Crowdsourcing a Word-Emotion Association Lexiconcited by 193
- 2021DEMix Layers: Disentangling Domains for Modular Language Modelingcited by 130
- 2019Multilingual is not enough: BERT for Finnishcited by 118
- 2017Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotationcited by 77
- 2017Word Affect Intensitiescited by 51
- 2020XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detectioncited by 31
- 2022Best Practices in the Creation and Use of Emotion Lexiconscited by 27
- 2016The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Paperscited by 20
- 2021Ethics Sheets for AI Taskscited by 18
- 2018A Survey on Sentiment and Emotion Analysis for Computational Literary Studiescited by 14
- 2019Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebankscited by 7
- 2022Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexiconscited by 6
2 more
- 2021Lemmatization of Historical Old Literary Finnish Texts in Modern Orthographycited by 5
- 2021The Current State of Finnish NLPcited by 5
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