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Sentiment analysis is a well-known natural language processing task that involves identifying the emotional tone or polarity of a given piece of text.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 1877–1901
1901
Earlier work this paper cites.
B. Liu, M. Hu, and J. Cheng, “Opinion observer: Analyzing and comparing opinions on the web,” in Proceedings of the 14th International Conference on World Wide Web , ser. WWW ’05. New York, NY, USA: Association for Computing Machinery, 2005, p. 342–351. [Online]. Available: https://doi.org/10.1145/1060745.1060797
2005
Earlier work this paper cites.
X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” in Advances in Neural Information Processing Systems , C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, Eds., vol. 28. Curran Associates, Inc., 2015
2015
Earlier work this paper cites.
L. Yue, W. Chen, X. Li, W. Zuo, and M. Yin, “A survey of sentiment analysis in social media,” Knowl. Inf. Syst. , vol. 60, no. 2, p. 617–663, aug 2019
2019
Earlier work this paper cites.
M. Chmielewski and S. C. Kucker, “An mturk crisis? shifts in data quality and the impact on study results,” Social Psychological and Personality Science , vol. 11, no. 4, pp. 464–473, 2020. [Online]. Available: https://doi.org/10.1177/1948550619875149
2020
Cited alongside, same era.
S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. V. Lin, T. Mihaylov, M. Ott, S. Shleifer, K. Shuster, D. Simig, P. S. Koura, A. Sridhar, T. Wang, and L. Zettlemoyer, “Opt: Open pre-trained transformer language models,” 2022
2022
Cited alongside, same era.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, P. Schuh, K. Shi, S. Tsvyashchenko, J. Maynez, A. Rao, P. Barnes, Y. Tay, N. Shazeer, V. Prabhakaran, E. Reif, N. Du, B. Hutchinson, R. Pope, J. Bradbury, J. Austin, M. Isard, G. Gur-Ari, P. Yin, T. Duke, A. Levskaya, S. Ghemawat, S. Dev, H. Michalewski, X. Garcia, V. Misra, K. Robinson, L. Fedus, D. Zhou, D. Ippolito, D. Luan, H. Lim, B. Zoph, A. Spiridonov, R. Sepassi, D. Dohan, S. Agrawal, M. Omernick, A. M. Dai, T. S. Pillai, M. Pellat, A. Lewkowycz, E. Moreira, R. Child, O. Polozov, K. Lee, Z. Zhou, X. Wang, B. Saeta, M. Diaz, O. Firat, M. Catasta, J. Wei, K. Meier-Hellstern, D. Eck, J. Dean, S. Petrov, and N. Fiedel, “Palm: Scaling language modeling with pathways,” 2022
H. Hettiarachchi, D. Al-Turkey, M. Adedoyin-Olowe, J. Bhogal, and M. M. Gaber, “TED-S: Twitter event data in sports and politics with aggregated sentiments,” Data , vol. 7, no. 7, 2022
2022
Later among the works it cites.
C. Qin, A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, and D. Yang, “Is chatgpt a general-purpose natural language processing task solver?” 2023
2023
Closest in time.
F. Gilardi, M. Alizadeh, and M. Kubli, “Chatgpt outperforms crowd-workers for text-annotation tasks,” 2023
2023
Closest in time.
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2022
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe, “Training language models to follow instructions with human feedback,” 2022
2022
Cited alongside, same era.