Papers with emotion analysis
Computationally Efficient Wasserstein Loss for Structured Labels (2021.eacl-srw)
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| Challenge: | Existing approaches to estimate the probability distribution of labels are based on tree-Wasserstein distance. |
| Approach: | They propose a tree-Wasserstein distance regularized LDL algorithm for hierarchical text classification tasks. |
| Outcome: | The proposed method performs well on synthetic and real-world datasets and compares favorably with the Sinkhorn algorithm in terms of computation time and memory usage. |
Emotional Intensity Estimation based on Writer’s Personality (2022.aacl-srw)
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| Challenge: | Existing emotion analysis models are difficult to accurately estimate the writer’s subjective emotions behind the text. |
| Approach: | They propose a method for personalized emotional intensity estimation based on a writer's personality test for Japanese SNS posts. |
| Outcome: | The proposed method improves on the existing method and the proposed hybrid model achieved state-of-the-art performance. |
Emotion Analysis from Texts (2023.eacl-tutorials)
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| Challenge: | Emotion analysis in text is a field of research that encompasses a set of various natural language processing tasks. |
| Approach: | This tutorial provides an overview of research from emotion psychology . it discusses the use cases of emotion analysis in text, their societal impact and ethical considerations . |
| Outcome: | This paper provides an overview of research from emotion psychology which sets the ground for choosing adequate NLP methodology. |
A Time Series Analysis of Emotional Loading in Central Bank Statements (D19-51)
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| Challenge: | a recent study has found that central bankers are communicating proactively to economic agents, resulting in a rapid growth of economic literature. |
| Approach: | They examine the affective content of central bank press statements using emotion analysis . they focus on the European Central Bank and the US Federal Reserve Bank . |
| Outcome: | The results show that the ECB and the Fed have strong emotional dimensions . the authors suggest that the use of emotion analysis could reveal latent emotions . |
An Architecture for Accelerated Large-Scale Inference of Transformer-Based Language Models (2021.naacl-industry)
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| Challenge: | a recent paper shows that attention-based language models can be used to train, evaluate, and perform inference on predictive models. |
| Approach: | They develop a machine learning architecture that can scale to a large volume of requests . they use a BERT model that is fine-tuned for emotion analysis . |
| Outcome: | The proposed architecture can scale to a large volume of requests with a minimum of 96 hours of running time. |
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions (P18-2)
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| Challenge: | a corpus of 5,605 manually annotated sentences in Chinese is described . emotion is an abstract and vague conception, which is often described by metaphor . |
| Approach: | They propose to construct a corpus of metaphors annotated with emotion in Chinese . they use an annotation scheme to include linguistic metaphors, emotional categories and intensity . |
| Outcome: | The proposed corpus contains 5,605 manually annotated sentences in Chinese . the authors show that the corpus is large enough to analyze emotions . |
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis (N19-1)
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Md Shad Akhtar, Dushyant Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
| Challenge: | Existing frameworks for sentiment and emotion analysis are not efficient for inter-task learning. |
| Approach: | They propose a multi-task learning framework that performs sentiment and emotion analysis together. |
| Outcome: | The proposed framework improves on a CMU-MOSEI dataset for sentiment and emotion analysis. |
Joint Learning for Emotion Classification and Emotion Cause Detection (D18-1)
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| Challenge: | Using a unified framework, we propose a joint approach for emotion classification and emotion cause detection. |
| Approach: | They propose a neural network-based joint approach for emotion classification and emotion cause detection which captures mutual benefits across the two sub-tasks. |
| Outcome: | The proposed approach can capture mutual benefits across two sub-tasks on Chinese microblogs. |
Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions (C18-1)
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| Challenge: | Emotion analysis and classification is a challenging task which has been tackled with relatively straight-forward approaches. |
| Approach: | They propose to annotate emotion trigger phrases and entities in the roles of experiencers, targets, and causes of the emotion in literature by Project Gutenberg. |
| Outcome: | The proposed corpus supports qualitative literary studies and digital humanities. |
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling (2024.lrec-main)
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| Challenge: | Existing models lack feature representations that capture the deep semantics of language and sensitivity to minor input variations, resulting in significant changes in the generated text. |
| Approach: | They propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots. |
| Outcome: | The proposed model outperforms existing models for generating empathetic embeddings, providing e-mpathetic and diverse responses. |
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations (2022.lrec-1)
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| Challenge: | Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. |
| Approach: | They propose that a classification setup for emotion analysis should be performed in an integrated manner, including the different semantic roles that participate in an emotion episode. |
| Outcome: | The proposed method reveals patterns in the co-occurrence of people’s emotions in interaction. |
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception (2020.lrec-1)
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| Challenge: | Fewer studies address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. |
| Approach: | They propose to annotate 5000 English news headlines with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, and the reader’s perception of the emotion of the headline. |
| Outcome: | The proposed method enables further research on emotion classification, emotion intensity prediction, emotion cause detection and supports qualitative studies. |
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction (2021.findings-acl)
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Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
| Challenge: | Detecting what emotions are expressed in text is a well-studied problem in natural language processing. |
| Approach: | They propose methods that combine common-sense knowledge with multi-task learning to perform joint emotion classification and emotion cause tagging. |
| Outcome: | The proposed models improve on both tasks when using common-sense reasoning and a multitask framework. |
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English (P19-1)
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| Challenge: | Existing studies on automatic recognition of emotions in text have achieved promising results, but there is a shortage of resources for non-English languages, with few exceptions, like Chinese. |
| Approach: | They propose to use a crowdsourced German emotion corpus to build a corpus similar to the English ISEAR emotion dataset. |
| Outcome: | The proposed model performs well in German and English, but lacks the resources for non-English languages. |
Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution (2024.acl-long)
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| Challenge: | Large language models reflect societal norms and biases, especially about gender. |
| Approach: | They propose to use large language models to examine gendered emotion attribution in five state-of-the-art LLMs to investigate whether emotions are genderes and whether they are influenced by societal stereotypes. |
| Outcome: | The proposed models exhibit gendered emotions, influenced by gender stereotypes, and the results are consistent with established research in psychology and gender studies. |
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions (2024.lrec-main)
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| 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 . |
DENS: A Dataset for Multi-class Emotion Analysis (D19-1)
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| 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. |
Towards Label-Agnostic Emotion Embeddings (2021.emnlp-main)
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| Challenge: | Existing representation schemes for emotion analysis are based on label formats, natural languages, and even disparate model architectures. |
| Approach: | They propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. |
| Outcome: | The proposed model performs well on a wide range of datasets without penalizing prediction quality. |
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain (2022.lrec-1)
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Haruya Suzuki, Yuto Miyauchi, Kazuki Akiyama, Tomoyuki Kajiwara, Takashi Ninomiya, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
| Challenge: | Existing studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. |
| Approach: | They extend the WRIME dataset with basic emotion intensity from both the writer's subjective and reader's perspective to include the Japanese sentiment polarity. |
| Outcome: | The proposed dataset is the first large-scale corpus to annotate both basic emotions and sentiment polarity labels from both the writer’s and reader’s perspectives. |
CULEMO: Cultural Lenses on Emotion - Benchmarking LLMs for Cross-Cultural Emotion Understanding (2025.acl-long)
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Tadesse Destaw Belay, Ahmed Haj Ahmed, Alvin C Grissom Ii, Iqra Ameer, Grigori Sidorov, Olga Kolesnikova, Seid Muhie Yimam
| Challenge: | Existing emotion benchmarks rely on keyword-based emotion recognition, overlooking cultural dimensions required for emotion understanding. |
| Approach: | They propose a benchmark to evaluate culturally-aware emotion prediction across six languages. |
| Outcome: | The proposed benchmark evaluates state-of-the-art LLMs on culture-aware emotion prediction and sentiment analysis tasks. |
Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding (2026.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection. |
| Approach: | They propose a large-scale model that combines large-level annotations with hyperbolic space to model human cognitive states. |
| Outcome: | The proposed model outperforms baseline models on cognitive dimensions on single dimension tasks while retaining strong hierarchical structure. |
MASIVE: Open-Ended Affective State Identification in English and Spanish (2024.emnlp-main)
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| Challenge: | Existing models that fail to understand cultural and language influences the meaning of emotional terms like "love" a new study shows that smaller finetuned models outperform much larger LLMs on region-specific span prediction tasks. |
| Approach: | They propose to use a reddit reddits dataset to identify a set of affective states . they find that smaller finetuned multilingual models outperform larger LLMs . |
| Outcome: | The proposed model outperforms larger models on span prediction task even on region-specific Spanish affective states. |
Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models (2025.findings-emnlp)
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| 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 . |
JoPR: Joint Emotion Perception and Reasoning for Conversational Emotion Recognition (2026.acl-long)
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| Challenge: | Existing methods for ERC lack human-like emotion reasoning and discrimination between similar emotions. |
| Approach: | They propose a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning for conversational emotion recognition. |
| Outcome: | The proposed model outperforms existing methods on three widely used datasets and shows that it is more intuitive and more accurate. |
ERCThinker: Fast-Slow Thinking for Emotion Recognition in Conversation (2026.acl-long)
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| Challenge: | Existing methods for ERC lack interpretability and shallow semantics capture deep semantics. |
| Approach: | They propose a Fast-Slow thinking framework for Emotion Recognition in Conversation . they use fine-grained emotion reasoning chains to capture deep semantics . |
| Outcome: | The proposed framework achieves state-of-the-art in explanation and judgment on a benchmark dataset. |