# Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters

arXiv: https://arxiv.org/abs/1903.12453 · HTML: https://mlsanity.com/paper/1903.12453/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 23 of 23)

- ["Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding](https://mlsanity.com/paper/2109.05438/) (2021)
- [Personality Understanding of Fictional Characters during Book Reading](https://mlsanity.com/paper/2305.10156/) (2023)
- [Multi-level Contrastive Learning for Script-based Character Understanding](https://mlsanity.com/paper/2310.13231/) (2023)
- [MBTI Personality Prediction for Fictional Characters Using Movie Scripts](https://mlsanity.com/paper/2210.10994/) (2022)
- [Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives](https://mlsanity.com/paper/2402.11051/) (2024)
- [Towards Inter-character Relationship-driven Story Generation](https://mlsanity.com/paper/2211.00676/) (2022)
- [SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues](https://mlsanity.com/paper/2106.01006/) (2021)
- [Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding](https://mlsanity.com/paper/2203.05711/) (2022)
- [x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations](https://mlsanity.com/paper/2203.10909/) (2022)
- [Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches](https://mlsanity.com/paper/2309.02092/) (2023)
- [Detecting and Grounding Important Characters in Visual Stories](https://mlsanity.com/paper/2303.17647/) (2023)
- [Emotion Stimulus Detection in German News Headlines](https://mlsanity.com/paper/2107.12920/) (2021)
- [Classifying Unreliable Narrators with Large Language Models](https://mlsanity.com/paper/2506.10231/) (2025)
- [Towards a Computational Analysis of Suspense: Detecting Dangerous Situations](https://mlsanity.com/paper/2305.06818/) (2023)
- [Evaluating Computational Representations of Character: An Austen Character Similarity Benchmark](https://mlsanity.com/paper/2408.16131/) (2024)
- [BookWorm: A Dataset for Character Description and Analysis](https://mlsanity.com/paper/2410.10372/) (2024)
- [Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding](https://mlsanity.com/paper/2406.13092/) (2024)
- [A City of Millions: Mapping Literary Social Networks At Scale](https://mlsanity.com/paper/2502.19590/) (2025)
- [Computational Storytelling and Emotions: A Survey](https://mlsanity.com/paper/2205.10967/) (2022)
- [Automatic Emotion Modelling in Written Stories](https://mlsanity.com/paper/2212.11382/) (2022)
- [Generating Visual Stories with Grounded and Coreferent Characters](https://mlsanity.com/paper/2409.13555/) (2024)
- [MARCUS: An Event-Centric NLP Pipeline that generates Character Arcs from Narratives](https://mlsanity.com/paper/2510.18201/) (2025)
- [CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories](https://mlsanity.com/paper/2606.22454/) (2026)

## Abstract (arXiv)

The development of a fictional plot is centered around characters who closely interact with each other forming dynamic social networks. In literature analysis, such networks have mostly been analyzed without particular relation types or focusing on roles which the characters take with respect to each other. We argue that an important aspect for the analysis of stories and their development is the emotion between characters. In this paper, we combine these aspects into a unified framework to classify emotional relationships of fictional characters. We formalize it as a new task and describe the annotation of a corpus, based on fan-fiction short stories. The extraction pipeline which we propose consists of character identification (which we treat as given by an oracle here) and the relation classification. For the latter, we provide results using several approaches previously proposed for relation identification with neural methods. The best result of 0.45 F1 is achieved with a GRU with character position indicators on the task of predicting undirected emotion relations in the associated social network graph.