| Challenge: | a new study examines the emotional journeys of characters in novels . previous studies have considered a novel as representing a single story arc . |
| Approach: | They analyze the emotion arcs of English literary novels using Utterance Emotion Dynamics . they find that narration and dialogue largely express disparate emotions through the course of a novel . |
| Outcome: | The analysis of English literary novels shows that narration and dialogue express disparate emotions . the commonalities or differences in the emotional arcs are more accurately captured by individual characters . |
Similar Papers
The Correlation Between Emotion in Text and Speech Segments is Limited: A Cross-Modal Study (2026.findings-eacl)
Copied to clipboard
| Challenge: | a recent study has shown that text-to-speech systems can capture human-like emotion, but they lack the ability to predict emotion in speech. |
| Approach: | They propose to use 8 large language models for identifying emotion in text and 2 audio models for emotion in speech to investigate the correlation between emotion and speech. |
| Outcome: | The proposed models perform well on emotion recognition from situational text and audiobooks, but show weak correlation for Valence only. |
Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Emotion arcs capture how an individual (or a population) feels over time. |
| Approach: | They compare machine-learning and Lexicon-Only methods to generate emotion arcs . they run experiments on 18 diverse datasets in 9 languages . |
| Outcome: | The proposed method is poor at instance level emotion classification, but highly accurate when aggregating information from hundreds of instances. |
Modeling Protagonist Emotions for Emotion-Aware Storytelling (2020.emnlp-main)
Copied to clipboard
| Challenge: | Cognitive scientists have pinpointed the central role of emotions in storytelling. |
| Approach: | They propose to use Emotion Supervision and two Emotion-Reinforced models to generate stories that follow the desired emotion arcs for the protagonist. |
| Outcome: | The proposed models generate stories that follow the desired emotion arcs without sacrificing story quality. |
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters (N19-1)
Copied to clipboard
| Challenge: | Existing literature analysis does not focus on roles of characters or on relationships between them. |
| Approach: | They propose to combine emotion and character identification into a unified framework for character network extraction from fictional texts. |
| Outcome: | The proposed task is based on fan-fiction short stories and is able to predict emotion relations in the extracted network graph. |
DENS: A Dataset for Multi-class Emotion Analysis (D19-1)
Copied to clipboard
| Challenge: | Existing sentence-level methods for emotion analysis are limited by the number of words in tweets and product reviews. |
| Approach: | They introduce a dataset for multi-class emotion analysis from long-form narratives in English . they use classic literature and modern online narratives available on Wattpad . |
| Outcome: | The proposed dataset provides a novel opportunity for emotion analysis that requires moving beyond sentence-level techniques. |
Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Using an emotion-annotated dataset, we aim to provide a resource for the scientific community to study the emotional intricacies of classical literature. |
| Approach: | They propose to provide an emotion-annotated dataset for classical literature and Western mythology using a multivariate time series and a deep learning masked language model. |
| Outcome: | The proposed dataset reveals compelling patterns and phenomena within the Iliad's emotional landscape. |
Literary Event Detection (P19-1)
Copied to clipboard
| Challenge: | a new dataset of literary events is presented to examine the nature of narratives . literature presents a number of challenges for existing systems, including complex narration . |
| Approach: | They propose a dataset of literary events that are depicted as taking place within the imagined space of a novel. |
| Outcome: | The proposed model achieves an F1 score of 73.9 for prestige and popularity . the best performing model achieve a score of 79.9 for prestige compared to the previous model . |
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on character-centric understanding of narratives focus on understanding the characters in the narrative, but these studies are limited to understanding only certain aspects of characters. |
| Approach: | They propose a dataset of literary pieces and their summaries paired with descriptions of characters that appear in them that are used to facilitate character-centric narrative understanding. |
| Outcome: | The proposed dataset includes literary pieces and their summaries paired with descriptions of characters that appear in them. |
When Words Smile: Generating Diverse Emotional Facial Expressions from Text (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems that generate only coarse facial expressions ignore the rich and dynamic nature of face-to-face communication. |
| Approach: | They propose an end-to-end text-to expression model that explicitly focuses on emotional dynamics. |
| Outcome: | The proposed model outperforms baselines on 15,000 text–3D expression pairs on a large-scale dataset. |
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books (N18-2)
Copied to clipboard
| Challenge: | We obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
| Approach: | They propose to model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. |
| Outcome: | The proposed model obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |