The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Transformer models for text-based emotion detection: a review of BERT-based approaches
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Cites
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 54,325
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
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
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334 · cited 1×
“The Circumplex of Affect proposed by Russell (1980) is a profound example of the dimensional emotion model that places emotions on a 2-dimensional circular wheel in an arousal-valence domain.”
BackgroundDeep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878
The Cognitive Structure of Emotions
Ortony, A., Clore, G. L., & Collins, A. (1988) · doi:10.1017/cbo9780511571299 · Cited by 6,208
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
Cited by
GPT is an effective tool for multilingual psychological text analysis
Rathje, S., Mirea, D.-M., Sucholutsky, I., Marjieh, R., Robertson, C. E., & Van Bavel, J. J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308950121 · Cited by 297
Sentiment analysis and opinion mining on educational data: A survey
Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2022.100003 · Cited by 215
Human-centered neural reasoning for subjective content processing: Hate speech, emotions, and humor
Kazienko, P., Bielaniewicz, J., Gruza, M., Kanclerz, K., Karanowski, K., Miłkowski, P., & Kocoń, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.01.010 · Cited by 38
Target-level sentiment analysis for news articles
Žitnik, S., Blagus, N., & Bajec, M. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108939 · Cited by 32
Li, Q., Cheng, X., Song, C., & Liu, T. (2023) · Symmetry · doi:10.3390/sym15030731 · Cited by 20
Metadata
- Authors
- Acheampong, F. A., Nunoo-Mensah, H., & Chen, W.
- Venue
- Artificial Intelligence Review
- Published
- 2021-02-08
- Volume
- 54
- Issue
- 8
- Pages
- 5789-5829
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W3128513378
- Cited by
- 659
- References
- 89
- FWCI
- 32.90
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Transformer models for text-based emotion detection: a review of BERT-based approaches. https://app.cochat.ai/papers/10.1007/s10462-021-09958-2
- The paper
- Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021). Transformer models for text-based emotion detection: a review of BERT-based approaches. Artificial Intelligence Review. https://doi.org/10.1007/s10462-021-09958-2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
