# Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books

arXiv: https://arxiv.org/abs/1805.09746 · HTML: https://mlsanity.com/paper/1805.09746/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2018.

## Built on by (most cited first, 6 of 6)

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- [Good Books are Complex Matters: Gauging Complexity Profiles Across Diverse Categories of Perceived Literary Quality](https://mlsanity.com/paper/2404.04022/) (2024)
- [Anime Popularity Prediction Before Huge Investments: a Multimodal Approach Using Deep Learning](https://mlsanity.com/paper/2406.16961/) (2024)
- [Modeling Story Expectations: A Generative Framework using LLMs](https://mlsanity.com/paper/2412.15239/) (2024)
- [Steering Emotional Dynamics for Art Therapy: Controllable Narrative Script Generation through Hierarchically Guided LLM Agents](https://mlsanity.com/paper/2606.16481/) (2026)

## Abstract (arXiv)

Books have the power to make us feel happiness, sadness, pain, surprise, or sorrow. An author's dexterity in the use of these emotions captivates readers and makes it difficult for them to put the book down. In this paper, we model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. We obtained the best weighted F1-score of 69% for predicting books' success in a multitask setting (simultaneously predicting success and genre of books).