# The emotional arcs of stories are dominated by six basic shapes

arXiv: https://arxiv.org/abs/1606.07772 · HTML: https://mlsanity.com/paper/1606.07772/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

## Built on by (most cited first, 30 of 71)

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- [Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis](https://mlsanity.com/paper/2306.02213/) (2023)
- [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://mlsanity.com/paper/2405.17633/) (2024)
- [The Shape of Beliefs: Geometry, Dynamics, and Interventions along Representation Manifolds of Language Models' Posteriors](https://mlsanity.com/paper/2602.02315/) (2026)
- [NarraBench: A Comprehensive Framework for Narrative Benchmarking](https://mlsanity.com/paper/2510.09869/) (2025)
- [Affective and Dynamic Beam Search for Story Generation](https://mlsanity.com/paper/2310.15079/) (2023)
- [The Emotion Dynamics of Literary Novels](https://mlsanity.com/paper/2403.02474/) (2024)
- [Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers](https://mlsanity.com/paper/2310.17369/) (2023)
- [Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts](https://mlsanity.com/paper/2112.01938/) (2021)
- [Compression ensembles quantify aesthetic complexity and the evolution of visual art](https://mlsanity.com/paper/2205.10271/) (2022)
- [Understanding Narratives through Dimensions of Analogy](https://mlsanity.com/paper/2206.07167/) (2022)
- [Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons](https://mlsanity.com/paper/2210.07381/) (2022)
- [Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community](https://mlsanity.com/paper/2301.08104/) (2023)
- [Acting as Inverse Inverse Planning](https://mlsanity.com/paper/2305.16913/) (2023)
- [Charting the Shapes of Stories with Game Theory](https://mlsanity.com/paper/2412.05747/) (2024)
- [SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs](https://mlsanity.com/paper/2110.09454/) (2021)
- [ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer](https://mlsanity.com/paper/2202.07305/) (2022)
- [Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning](https://mlsanity.com/paper/2406.02251/) (2024)
- [Narrix: Remixing Narrative Strategies from Examples for Story Writing](https://mlsanity.com/paper/2604.07643/) (2026)
- [Persistence Homology of TEDtalk: Do Sentence Embeddings Have a Topological Shape?](https://mlsanity.com/paper/2103.14131/) (2021)
- [All Stories Are One Story: Emotional Arc Guided Procedural Game Level Generation](https://mlsanity.com/paper/2508.02132/) (2025)
- [SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations](https://mlsanity.com/paper/2108.11523/) (2021)
- [Computational analyses of the topics, sentiments, literariness, creativity and beauty of texts in a large Corpus of English Literature](https://mlsanity.com/paper/2201.04356/) (2022)
- [A decomposition of book structure through ousiometric fluctuations in cumulative word-time](https://mlsanity.com/paper/2208.09496/) (2022)
- [A City of Millions: Mapping Literary Social Networks At Scale](https://mlsanity.com/paper/2502.19590/) (2025)

## Abstract (arXiv)

Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture's evolution through its texts using a "big data" lens. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories and forming patterns that are meaningful to us. Here, by classifying the emotional arcs for a filtered subset of 1,327 stories from Project Gutenberg's fiction collection, we find a set of six core emotional arcs which form the essential building blocks of complex emotional trajectories. We strengthen our findings by separately applying Matrix decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads.