Transformer based multilingual joint learning framework for code-mixed and english sentiment analysis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10844-023-00808-x.pdf
Reference84 articles.
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2. Akhtar, M. S., Ekbal, A., & Cambria, E. (2020). How intense are you? predicting intensities of emotions and sentiments using stacked ensemble. IEEE Computational Intelligence Magazine, 15(1), 64–75. https://doi.org/10.1109/MCI.2019.2954667
3. Akhtar, M.S., Ghosal, D., Ekbal, A., et al. (2018). A multi-task ensemble framework for emotion, sentiment and intensity prediction. https://doi.org/10.48550/arXiv.1808.01216
4. Akhtar, M. S., Gupta, D., Ekbal, A., et al. (2017). Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis. Knowledge-Based Systems, 125, 116–135. https://doi.org/10.1016/j.knosys.2017.03.020
5. Akhtar, M.S., Kumar, A., Ghosal, D., et al. (2017). A multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 540–546. Association for Computational Linguistics. https://doi.org/10.18653/v1/D17-1057
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