# From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales

arXiv: https://arxiv.org/abs/1309.5909 · HTML: https://mlsanity.com/paper/1309.5909/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2013.

## Built on by (most cited first, 16 of 16)

- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/2109.08256/) (2021)
- [Emotion Dynamics in Movie Dialogues](https://mlsanity.com/paper/2103.01345/) (2021)
- [Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis](https://mlsanity.com/paper/2306.02213/) (2023)
- [Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers](https://mlsanity.com/paper/2310.17369/) (2023)
- [Fearful Falcons and Angry Llamas: Emotion Category Annotations of Arguments by Humans and LLMs](https://mlsanity.com/paper/2412.15993/) (2024)
- [WorryWords: Norms of Anxiety Association for over 44k English Words](https://mlsanity.com/paper/2411.03966/) (2024)
- [Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons](https://mlsanity.com/paper/2210.07381/) (2022)
- [M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations](https://mlsanity.com/paper/2302.09418/) (2023)
- [Prosody Analysis of Audiobooks](https://mlsanity.com/paper/2310.06930/) (2023)
- [A Case Study of Deep Learning Based Multi-Modal Methods for Predicting the Age-Suitability Rating of Movie Trailers](https://mlsanity.com/paper/2101.11704/) (2021)
- [Utterance Emotion Dynamics in Children's Poems: Emotional Changes Across Age](https://mlsanity.com/paper/2306.05387/) (2023)
- [Machine Learning and Statistical Approaches to Measuring Similarity of Political Parties](https://mlsanity.com/paper/2306.03079/) (2023)
- [Topic Bias in Emotion Classification](https://mlsanity.com/paper/2312.09043/) (2023)
- [Semantic Novelty at Scale: Narrative Shape Taxonomy and Readership Prediction in 28,606 Books](https://mlsanity.com/paper/2602.20647/) (2026)
- [Sentiment Analysis of German Sign Language Fairy Tales](https://mlsanity.com/paper/2604.16138/) (2026)

## Abstract (arXiv)

Today we have access to unprecedented amounts of literary texts. However, search still relies heavily on key words. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in both individual books and across very large collections. We introduce the concept of emotion word density, and using the Brothers Grimm fairy tales as example, we show how collections of text can be organized for better search. Using the Google Books Corpus we show how to determine an entity's emotion associations from co-occurring words. Finally, we compare emotion words in fairy tales and novels, to show that fairy tales have a much wider range of emotion word densities than novels.