A Simple and Interactive Transformer for Fine-Grained Emotion Detection
Abstract
Fine-grained emotion detection is a long-standing task in sentiment analysis. One of the crucial challenges in this task is to capture emotion correlations. Previous studies either ignore this challenge or resort to a chain-link structure (e.g. seq2seq), both of which encounter some thorny issues in reality. Unlike previous studies, we approach this challenge by proposing a masked language modeling like mechanism, named masked emotion modeling (MEM), which can capture emotion correlations in a simple but effective manner. Specifically, we augment the transformer model with emotion states and require the transformer model to recover masked emotion states. Furthermore, to fully enhance the effect of the proposed MEM, we also introduce a taxonomy-guided sampling strategy at the training phase and an iterative decoding strategy at the test phase. On two widely used datasets, we demonstrate that our method excels at capturing emotion correlations and achieves state-of-the-art results.
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Results on the GoEmotions dataset show improved performance of transformer-based models when integrated with EmoVis_lin.
“results on the GoEmotions dataset showing improved performance of transformer-based models when integrated with EmoVis lin [30], [49]”
From Emotion Classification With Visibility Graphs (2025) · cites this paper as [49]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
(2014) · Cited by 33,419 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
Layer NormalizationPreprint
Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,745 · see citing works
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522 · see citing works
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643 · see citing works
Masked Autoencoders Are Scalable Vision Learners
He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01553 · Cited by 8,489 · see citing works
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096 · see citing works
Classifier chains for multi-label classification
Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011) · Machine Learning · doi:10.1007/s10994-011-5256-5 · Cited by 2,312 · see citing works
A GENERAL PSYCHOEVOLUTIONARY THEORY OF EMOTION
Plutchik, R. (1980) · Elsevier eBooks · doi:10.1016/b978-0-12-558701-3.50007-7 · Cited by 2,240 · see citing works
Improved Boosting Algorithms Using Confidence-rated Predictions
Schapire, R. E., & Singer, Y. (1999) · Machine Learning · doi:10.1023/a:1007614523901 · Cited by 1,964 · see citing works
BEiT: BERT Pre-Training of Image Transformers
Bao, H., Dong, L., Piao, S., & Wei, F. (2021) · arXiv · doi:10.48550/arxiv.2106.08254 · Cited by 1,392 · see citing works
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference
Schick, T., & Schütze, H. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.20 · Cited by 1,305 · see citing works
New Avenues in Opinion Mining and Sentiment Analysis
Cambria, E., Schuller, B. W., Xia, Y., & Havasi, C. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.30 · Cited by 1,289 · see citing works
Facial Expressions of Emotion: New Findings, New Questions
Ekman, P. (1992) · Psychological Science · doi:10.1111/j.1467-9280.1992.tb00253.x · Cited by 918 · see citing works
A Computer Program for Classifying Plants
Rogers, D. J., & Tanimoto, T. T. (1960) · Science · doi:10.1126/science.132.3434.1115 · Cited by 874 · see citing works
GoEmotions: A Dataset of Fine-Grained Emotions
Demszky, D., Movshovitz-Attias, D., Ko, J., Cowen, A., Nemade, G., & Ravi, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.372 · Cited by 873 · see citing works
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.185 · Cited by 747 · see citing works
SemEval-2018 Task 1: Affect in Tweets
Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018) · Proceedings of The 12th International Workshop on Semantic Evaluation · doi:10.18653/v1/s18-1001 · Cited by 746 · see citing works
Felbo, B., Mislove, A. E., Søgaard, A., Rahwan, I., & Lehmann, S. (2017) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 712 · see citing works
Order Matters: Sequence to sequence for setsPreprint
Vinyals, O., Samy Bengio, A., & Kudlur, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06391 · Cited by 696 · see citing works
General Multi-label Image Classification with Transformers
Lanchantin, J., Wang, T., Ordóñez, V., & Qi, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01621 · Cited by 378 · see citing works
Transformer Feed-Forward Layers Are Key-Value Memories
Geva, M., Schuster, R., Berant, J., & Levy, O. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.446 · Cited by 353 · see citing works
SGM: Sequence Generation Model for Multi-label ClassificationPreprint
Yang, P., Sun, X. S., Li, W., Ma, S., Wu, W., & Wang, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04822 · Cited by 346 · see citing works
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
Tian, H., Gao, C., Xiao, X., Liu, H., He, B., Wu, H. R., Wang, H., & Wu, F. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.374 · Cited by 299 · see citing works
Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold.
Saif, H., Fernández, M., He, Y., & Alani, H. (2013) · Open Research Online - ORO (The Open University) · Cited by 206 · see citing works
Emotion classification using massive examples extracted from the web
Tokuhisa, R., Inui, K., & Matsumoto, Y. (2008) · International Conference on Computational Linguistics · doi:10.3115/1599081.1599192 · Cited by 197 · see citing works
Visualizing and Understanding the Effectiveness of BERT
Hao, Y., Dong, L., Wei, F., & Xu, K. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1424 · Cited by 196 · see citing works
Latent Emotion Memory for Multi-Label Emotion Classification
Fei, H., Zhang, Y., Ren, Y., & Ji, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6271 · Cited by 144 · see citing works
Baziotis, C., Nikolaos, A., Chronopoulou, A., Kolovou, A., Paraskevopoulos, G., Ellinas, N., Narayanan, S., & Potamianos, A. (2018) · Proceedings of The 12th International Workshop on Semantic Evaluation · doi:10.18653/v1/s18-1037 · Cited by 142 · see citing works
Reflections on Cognition and Parallel Distributed Processing
(1986) · The MIT Press eBooks · doi:10.7551/mitpress/5236.003.0018 · Cited by 110 · see citing works
STATISTICAL LANGUAGE MODELS BASED ON NEURAL NETWORKS
Mikolov, T. (2012) · Brno University of Technology Digital Library (Brno University of Technology) · Cited by 107 · see citing works
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
Alhuzali, H., & Ananiadou, S. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.135 · Cited by 87 · see citing works
Yu, J., Marujo, L., Jiang, J., Karuturi, P., & Brendel, W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1137 · Cited by 75 · see citing works
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis
Zhou, J., Tian, J., Wang, R., Wu, Y., Xiao, W., & He, L. J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.49 · Cited by 75 · see citing works
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification
Yang, P., Luo, F., Ma, S., Lin, J., & Sun, X. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1518 · Cited by 70 · see citing works
Sentence-level Emotion Classification with Label and Context Dependence
Li, S., Huang, L., Wang, R., & Zhou, G. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1101 · Cited by 66 · see citing works
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification
He, H., & Xia, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99495-6_21 · Cited by 61 · see citing works
Reflections on Sentiment/Opinion Analysis
Li, J., & Hovy, E. H. (2017) · Socio-affective computing · doi:10.1007/978-3-319-55394-8_3 · Cited by 59 · see citing works
Firdaus, M., Chauhan, H., Ekbal, A., & Bhattacharyya, P. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.393 · Cited by 56 · see citing works
Dimensional Emotion Detection from Categorical Emotion
Park, S., Kim, J., Ye, S., Jeon, J., Park, H. Y., & Oh, A. H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.358 · Cited by 52 · see citing works
SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets
Duppada, V., Jain, R., & Hiray, S. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1002 · Cited by 51 · see citing works
Multi-label Chinese Microblog Emotion Classification via Convolutional Neural Network
Wang, Y., Feng, S., Wang, D., Yu, G., & Zhang, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45814-4_46 · Cited by 39 · see citing works
Latent Discriminative Models for Social Emotion Detection with Emotional Dependency
Quan, X., Wang, Q., Zhang, Y., Si, L., & Wenyin, L. (2015) · ACM Transactions on Information Systems · doi:10.1145/2749459 · Cited by 36 · see citing works
Relevant Emotion Ranking from Text Constrained with Emotion Relationships
Zhou, D., Yang, Y., & He, Y. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1052 · Cited by 36 · see citing works
Ying, W., Xiang, R., & Lu, Q. (2019) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/d19-5541 · Cited by 33 · see citing works
EmoGraph: Capturing Emotion Correlations using Graph NetworksPreprint
Xu, P., Liu, Z., Winata, G. I., Lin, Z., & Fung, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.09378 · Cited by 28 · see citing works
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
Pergola, G., Kochkina, E., Gui, L., Liakata, M., & He, Y. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.169 · Cited by 25 · see citing works
On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies
Zhang, T., & Hashimoto, T. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.404 · Cited by 23 · see citing works
Autoencoder for Semisupervised Multiple Emotion Detection of Conversation Transcripts
Phan, D. A., Matsumoto, Y., & Shindo, H. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2885304 · Cited by 21 · see citing works
Multiple Emotions Detection in Conversation Transcripts
Phan, D. A., Shindo, H., & Matsumoto, Y. (2016) · Waseda University Repository (Waseda University) · Cited by 16 · see citing works
eMLM: A New Pre-training Objective for Emotion Related Tasks
Sosea, T., & Caragea, C. (2021) · doi:10.18653/v1/2021.acl-short.38 · Cited by 11 · see citing works
Feedback-Prop: Convolutional Neural Network Inference Under Partial Evidence
Wang, T., Yamaguchi, K., & Ordóñez, V. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00100 · Cited by 10 · see citing works
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Path-Aware Knowledge Injection for Fine-Grained Emotion Recognition in Mental Health Counseling
Zhang, H., & Niu, D. (2025) · IEEE Access · doi:10.1109/access.2025.3599547 · Cited by 3 · see citing works
Emotion Classification With Visibility Graphs
Şimşek, E., Topcu, A., Koç, E., Saritas, E. U., & Koç, A. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3567033 · Cited by 2 · see citing works
Systematic Review as Recipe Book for Nuanced Emotion Detection in Noisy TextPreprint
Winchell, J., Iqbal, J., Ajanwachuku, N. C., Farra, M., Galal, M., & Machiraju, A. (2026) · doi:10.66977/xsci.2610.000g
- Authors
- Sui, D., Li, B., Yang, H., Chen, Y., Liu, K., Chu, D., & Zhao, J.
- Affiliations
- Harbin Institute of Technology (CN)
- Peking University (CN)
- Chinese Academy of Sciences (CN)
- Venue
- IEEE Transactions on Audio, Speech and Language Processing
- Published
- 2025
- Volume
- 33
- Pages
- 347-358
- ISSN
- 2998-4173 (ISSN-L)
- OpenAlex
- W4403863526
- Cited by
- 2
- References
- 60
- Citations by year
- 2025: 2
- FWCI
- 0.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Audio Speech and Language Processing (publishedVersion)
- Funders
- National Key Research and Development Program of China (2022ZD0160503)
- National Natural Science Foundation of China (6217625, 62306087, 72203005, 72450002)
- Natural Science Foundation of Shandong Province (ZR2023QF154)
- Special Funding Program of Shandong Taishan Scholars Project
- Open Project of Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University (MMC202420)
- Beijing Philosophy and Social Science Foundation (24DTR024)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- emotional state, masked language modeling, iterative decoding, sentiment analysis
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