# SpanEmo: Casting Multi-label Emotion Classification as Span-prediction

arXiv: https://arxiv.org/abs/2101.10038 · HTML: https://mlsanity.com/paper/2101.10038/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media](https://mlsanity.com/paper/2007.13184/) (2020)
- [Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts](https://mlsanity.com/paper/1906.01267/) (2019)
- [Emotion Intensities in Tweets](https://mlsanity.com/paper/1708.03696/) (2017)
- [NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning](https://mlsanity.com/paper/1804.06658/) (2018)
- [Learning Deep Latent Spaces for Multi-Label Classification](https://mlsanity.com/paper/1707.00418/) (2017)
- [Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification](https://mlsanity.com/paper/1802.00891/) (2018)
- [EmoGraph: Capturing Emotion Correlations using Graph Networks](https://mlsanity.com/paper/2008.09378/) (2020)
- [Using Nuances of Emotion to Identify Personality](https://mlsanity.com/paper/1309.6352/) (2013)

## Built on by (most cited first, 30 of 37)

- [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024)
- [Whose Emotions and Moral Sentiments Do Language Models Reflect?](https://mlsanity.com/paper/2402.11114/) (2024)
- [Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion](https://mlsanity.com/paper/2210.15842/) (2022)
- [Emotion fusion for mental illness detection from social media: A survey](https://mlsanity.com/paper/2304.09493/) (2023)
- [Large Language Models Do Multi-Label Classification Differently](https://mlsanity.com/paper/2505.17510/) (2025)
- [An Effective Deployment of Contrastive Learning in Multi-label Text Classification](https://mlsanity.com/paper/2212.00552/) (2022)
- [Multi-Label Classification for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2406.04461/) (2024)
- [A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions](https://mlsanity.com/paper/2305.07665/) (2023)
- [TempoFormer: A Transformer for Temporally-aware Representations in Change Detection](https://mlsanity.com/paper/2408.15689/) (2024)
- [Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training](https://mlsanity.com/paper/2111.06181/) (2021)
- [Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies](https://mlsanity.com/paper/2311.09687/) (2023)
- [Polarized Online Discourse on Abortion: Frames and Hostile Expressions among Liberals and Conservatives](https://mlsanity.com/paper/2311.16831/) (2023)
- [Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification](https://mlsanity.com/paper/2404.08720/) (2024)
- [Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing](https://mlsanity.com/paper/2409.19025/) (2024)
- [Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors](https://mlsanity.com/paper/2410.13776/) (2024)
- [Estimating Causal Effects of Text Interventions Leveraging LLMs](https://mlsanity.com/paper/2410.21474/) (2024)
- [Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit](https://mlsanity.com/paper/2212.00339/) (2022)
- [Leveraging Machine Learning to Identify Gendered Stereotypes and Body Image Concerns on Diet and Fitness Online Forums](https://mlsanity.com/paper/2407.03551/) (2024)
- [EmoDNN: Understanding emotions from short texts through a deep neural network ensemble](https://mlsanity.com/paper/2106.01706/) (2021)
- [Cross Domain Emotion Recognition using Few Shot Knowledge Transfer](https://mlsanity.com/paper/2110.05021/) (2021)
- [Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment](https://mlsanity.com/paper/2407.01784/) (2024)
- [Transformer based ensemble for emotion detection](https://mlsanity.com/paper/2203.11899/) (2022)
- [LyS at SemEval-2024 Task 3: An Early Prototype for End-to-End Multimodal Emotion Linking as Graph-Based Parsing](https://mlsanity.com/paper/2405.06483/) (2024)
- [Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health care](https://mlsanity.com/paper/2406.15966/) (2024)
- [Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation](https://mlsanity.com/paper/2407.10091/) (2024)
- [Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function](https://mlsanity.com/paper/2410.03979/) (2024)
- [EiCAP: Beyond Fluency, Probing and Improving Emotional Intelligence in LLMs via Psychologically Grounded Multi-Turn Dialogue](https://mlsanity.com/paper/2508.06196/) (2025)
- [Leveraging IndoBERT and DistilBERT for Indonesian Emotion Classification in E-Commerce Reviews](https://mlsanity.com/paper/2509.14611/) (2025)
- [Lexico-semantic and affective modelling of Spanish poetry: A semi-supervised learning approach](https://mlsanity.com/paper/2109.04152/) (2021)
- [Auto-ABSA: Cross-Domain Aspect Detection and Sentiment Analysis Using Auxiliary Sentences](https://mlsanity.com/paper/2202.00484/) (2022)

## Abstract (arXiv)

Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and security. Current approaches to ER, mainly classify emotions independently without considering that emotions can co-exist. Such approaches overlook potential ambiguities, in which multiple emotions overlap. We propose a new model "SpanEmo" casting multi-label emotion classification as span-prediction, which can aid ER models to learn associations between labels and words in a sentence. Furthermore, we introduce a loss function focused on modelling multiple co-existing emotions in the input sentence. Experiments performed on the SemEval2018 multi-label emotion data over three language sets (i.e., English, Arabic and Spanish) demonstrate our method's effectiveness. Finally, we present different analyses that illustrate the benefits of our method in terms of improving the model performance and learning meaningful associations between emotion classes and words in the sentence.