My Heart Skipped a Beat! Recognizing Expressions of Embodied Emotion in Natural Language (2024.naacl-long)
Copied to clipboard
| Challenge: | a new task is needed to recognize physical manifestations of emotions in natural language . physical manifestation of emotions affects not only our mental state but also our physical state . |
| Approach: | They propose a task to recognize expressions of embodied emotion in natural language . they use body part mentions with human annotations to extract emotional manner expressions . |
| Outcome: | The proposed model can train without gold data and improve performance with gold data. |
Similar Papers
CHEER-Ekman: Fine-grained Embodied Emotion Classification (2025.acl-short)
Copied to clipboard
| Challenge: | Emotions manifest through physical experiences and bodily reactions, yet identifying such embodied emotions in text remains understudied. |
| Approach: | They propose to extend existing binary embodied emotion dataset with Ekman’s six basic emotion categories. |
| Outcome: | The proposed dataset outperforms existing methods with large language models. |
Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | ELENA is a framework for embodied emotion analysis using large vision language models . ELEna uses attention maps and a persistent bias towards the facial region . |
| Approach: | They propose a framework that utilizes large vision language models to generate ELENA . they propose to use attention maps to describe emotional reactions from body parts . |
| Outcome: | The proposed framework outperforms baseline models without fine-tuning . it uses large vision language models to generate embodied emotion narratives . |
Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)
Copied to clipboard
| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
| Approach: | They propose to use unsupervised, supervised, and finally supervised methods to extract emotional associations from pretrained vectors and models. |
| Outcome: | The proposed method shows higher correlation with ground truth ratings than state-of-the-art lexicons based on labeled data. |
The Language of Interoception: Examining Embodiment and Emotion Through a Corpus of Body Part Mentions (2025.findings-emnlp)
Copied to clipboard
| Challenge: | 5% to 10% of posts include body part mentions in English text . text containing BPMs tends to be more emotionally charged, even when the BPM is not used to describe a physical reaction to the emotion in the text. |
| Approach: | They create corpora of body part mentions in online English text with human annotations for the emotions of the person whose body part is mentioned. |
| Outcome: | The proposed study is the first to investigate the connection between emotion, embodiment, and everyday language in a large sample of natural language data. |
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions (2024.lrec-main)
Copied to clipboard
| Challenge: | Emotion analysis (EA) is a rapidly growing field in natural language processing . there is no consensus on scope, direction, or methods for EA . |
| Approach: | They review 154 relevant NLP papers on emotion analysis from the last decade . they ask: how are EA tasks defined in NLP? what are the most prominent emotion frameworks and which emotions are modeled? |
| Outcome: | The authors examine 154 relevant NLP papers on emotion analysis from the last decade . they find that there is no consensus on scope, direction, or methods . |
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. |
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluations of emotional intelligence in large language models (LLMs) focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs’ overall emotional intelligence. |
| Approach: | They propose a framework for evaluating the emotional intelligence of large language models (LLMs) that includes four distinct tasks: Key Event Recognition, Mixed Event Recognition and Implicit Emotional Recognition. |
| Outcome: | The proposed framework includes four distinct tasks: Key Event Recognition, Mixed Event Recognition and Implicit Emotional Recognition. |
A (Psycho-)Linguistically Motivated Scheme for Annotating and Exploring Emotions in a Genre-Diverse Corpus (2022.lrec-1)
Copied to clipboard
| Challenge: | Using a linguistic perspective, emotion annotation is considered a difficult task because of the lack of consensus on emotional categories, the fuzziness of boundaries between them or the great variability of emotion expressions types. |
| Approach: | They propose a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in french. |
| Outcome: | The proposed method clarifies the main concepts implied by the analysis of emotions as they are expressed in texts and performs a manual annotation campaign on a corpus of 1,594 texts (ca. 515K tokens) of different genres. |
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent studies have demonstrated that Large Language Models possess a form of emotional intelligence, capable of interpreting emotional stimuli in text. |
| Approach: | They propose a method that translates speech characteristics into natural language descriptions and integrates them into LLMs to perform multimodal emotion analysis via text prompts. |
| Outcome: | The proposed method outperforms baseline models that require structural modifications on two datasets showing significant improvements in emotion recognition accuracy. |
Emotion-Infused Models for Explainable Psychological Stress Detection (2021.naacl-main)
Copied to clipboard
| Challenge: | a new study examines the use of emotion detection for detecting psychological stress in online posts . traditional multi-task learning and emotion-based language model fine-tuning are used to improve the model . |
| Approach: | They propose to use a semantically related task, emotion detection, for detecting psychological stress in online posts . they propose multi-task learning and emotion-based language model fine-tuning to improve the model . |
| Outcome: | The proposed model is more explainable and human-like than a black-box model . the proposed model mirrors psychological components of stress, the authors show . |