# Affect as a proxy for literary mood

arXiv: https://arxiv.org/abs/2304.02894 · HTML: https://mlsanity.com/paper/2304.02894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [DEMix Layers: Disentangling Domains for Modular Language Modeling](https://mlsanity.com/paper/2108.05036/) (2021)
- [Multilingual is not enough: BERT for Finnish](https://mlsanity.com/paper/1912.07076/) (2019)
- [Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation](https://mlsanity.com/paper/1712.01765/) (2017)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](https://mlsanity.com/paper/2011.01612/) (2020)
- [Best Practices in the Creation and Use of Emotion Lexicons](https://mlsanity.com/paper/2210.07206/) (2022)
- [The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers](https://mlsanity.com/paper/1612.01556/) (2016)
- [Ethics Sheets for AI Tasks](https://mlsanity.com/paper/2107.01183/) (2021)
- [A Survey on Sentiment and Emotion Analysis for Computational Literary Studies](https://mlsanity.com/paper/1808.03137/) (2018)
- [Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks](https://mlsanity.com/paper/1902.00972/) (2019)
- [Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons](https://mlsanity.com/paper/2210.07381/) (2022)
- [Lemmatization of Historical Old Literary Finnish Texts in Modern Orthography](https://mlsanity.com/paper/2107.03266/) (2021)
- [The Current State of Finnish NLP](https://mlsanity.com/paper/2109.11326/) (2021)

## 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.