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Artificial Intelligence (AI) software systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human biases.
E. Brill, “Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging,” Comput. Linguist. , vol. 21, no. 4, p. 543–565, Dec. 1995
1995
Earlier work this paper cites.
W. M. Soon, H. T. Ng, and D. C. Y. Lim, “A machine learning approach to coreference resolution of noun phrases,” Computational Linguistics , vol. 27, no. 4, pp. 521–544, 2001. [Online]. Available: https://www.aclweb.org/anthology/J01-4004
2001
Earlier work this paper cites.
B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? sentiment classification using machine learning techniques,” in Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10 , ser. EMNLP ’02. USA: Association for Computational Linguistics, 2002, p. 79–86. [Online]. Available: https://doi.org/10.3115/1118693.1118704
2002
Earlier work this paper cites.
P. D. Turney, “Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews,” in Proceedings of the 40th Annual Meeting on Association for Computational Linguistics , ser. ACL ’02. USA: Association for Computational Linguistics, 2002, p. 417–424. [Online]. Available: https://doi.org/10.3115/1073083.1073153
2002
Earlier work this paper cites.
J. Zhao, T. Wang, M. Yatskar, V. Ordonez, and K.-W. Chang, “Gender bias in coreference resolution: Evaluation and debiasing methods,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) . New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 15–20. [Online]. Available: https://www.aclweb.org/anthology/N18-2003
2003
Earlier work this paper cites.
D. Nadeau and S. Sekine, “A survey of named entity recognition and classification,” Lingvisticae Investigationes , vol. 30, pp. 3–26, 2007
2007
Earlier work this paper cites.
J. Nivre and S. Kübler, “Dependency parsing,” Synthesis Lectures on Human Language Technologies , vol. 2, 01 2009
2009
Earlier work this paper cites.
A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” pp. 1–12, 2009. [Online]. Available: http://www.stanford.edu/~alecmgo/papers/TwitterDistantSupervision09.pdf
2009
Earlier work this paper cites.
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies . Portland, Oregon, USA: Association for Computational Linguistics, June 2011, pp. 142–150. [Online]. Available: http://www.aclweb.org/anthology/P11-1015
2011
Earlier work this paper cites.
M. Rambocas, “Marketing research: The role of sentiment analysis,” FEP WORKING PAPER SERIES , 04 2013
2013
Earlier work this paper cites.
N. Altrabsheh, M. Gaber, and E. Haig, “Sa-e: Sentiment analysis for education,” in Frontiers in Artificial Intelligence and Applications , vol. 255, 06 2013
2013
Earlier work this paper cites.
W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams engineering journal , vol. 5, no. 4, pp. 1093–1113, 2014
2014
Earlier work this paper cites.
D. Chen and C. Manning, “A fast and accurate dependency parser using neural networks,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Doha, Qatar: Association for Computational Linguistics, Oct. 2014, pp. 740–750. [Online]. Available: https://www.aclweb.org/anthology/D14-1082
2014
Earlier work this paper cites.
A. Abbasi, A. Hassan, and M. Dhar, “Benchmarking Twitter sentiment analysis tools,” in Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14) . Reykjavik, Iceland: European Language Resources Association (ELRA), May 2014, pp. 823–829. [Online]. Available: http://www.lrec-conf.org/proceedings/lrec2014/pdf/483_Paper.pdf
2014
Earlier work this paper cites.
M. Day and C. Lee, “Deep learning for financial sentiment analysis on finance news providers,” in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) , 2016, pp. 1127–1134
2016
Earlier work this paper cites.
V. S. Gupta and S. Kohli, “Twitter sentiment analysis in healthcare using hadoop and r,” in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) , 2016, pp. 3766–3772
2016
Earlier work this paper cites.
T. Bolukbasi, K.-W. Chang, J. Zou, V. Saligrama, and A. Kalai, “Man is to computer programmer as woman is to homemaker? debiasing word embeddings,” in Proceedings of the 30th International Conference on Neural Information Processing Systems , ser. NIPS’16. Red Hook, NY, USA: Curran Associates Inc., 2016, p. 4356–4364
2016
Earlier work this paper cites.
A. Caliskan-Islam, J. Bryson, and A. Narayanan, “Semantics derived automatically from language corpora necessarily contain human biases,” Science , vol. 356, 08 2016
2016
Earlier work this paper cites.
M. Hardt, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in neural information processing systems , 2016, pp. 3315–3323
2016
Earlier work this paper cites.
S. Galhotra, Y. Brun, and A. Meliou, “Fairness testing: testing software for discrimination,” in Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering , 2017, pp. 498–510
2017
Earlier work this paper cites.
F. Tramer, V. Atlidakis, R. Geambasu, D. Hsu, J.-P. Hubaux, M. Humbert, A. Juels, and H. Lin, “Fairtest: Discovering unwarranted associations in data-driven applications,” in 2017 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 2017, pp. 401–416
2017
Earlier work this paper cites.
M. Haselmayer and M. Jenny, “Sentiment analysis of political communication: combining a dictionary approach with crowdcoding,” Quality & Quantity , vol. 51, pp. 2623 – 2646, 2017
2017
Earlier work this paper cites.
S. Sohangir, D. Wang, A. Pomeranets, and T. M. Khoshgoftaar, “Big data: Deep learning for financial sentiment analysis,” Journal of Big Data , vol. 5, pp. 1–25, 2017
2017
Earlier work this paper cites.
S. Rani and P. Kumar, “A sentiment analysis system to improve teaching and learning,” Computer , vol. 50, no. 05, pp. 36–43, may 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
E. Cambria, S. Poria, A. Gelbukh, and M. Thelwall, “Sentiment analysis is a big suitcase,” IEEE Intelligent Systems , vol. 32, no. 6, pp. 74–80, 2017
2017
Earlier work this paper cites.
M. J. Kusner, J. Loftus, C. Russell, and R. Silva, “Counterfactual fairness,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf
2017
Cited alongside, same era.
A. Caliskan, J. Bryson, and A. Narayanan, “Semantics derived automatically from language corpora contain human-like biases,” Science , vol. 356, pp. 183–186, 04 2017
2017
Cited alongside, same era.
S. Udeshi, P. Arora, and S. Chattopadhyay, “Automated directed fairness testing,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering , 2018, pp. 98–108
2018
Cited alongside, same era.
J. A. Caetano, H. S. Lima, M. F. Santos, and H. T. Marques-Neto, “Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 american presidential election,” Journal of Internet Services and Applications , vol. 9, pp. 1–15, 2018
2019
Later among the works it cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in 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) , 2019, pp. 4171–4186
2019
Later among the works it cites.
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” 2019
2019
Later among the works it cites.
T. Thongtan and T. Phienthrakul, “Sentiment classification using document embeddings trained with cosine similarity,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop , 2019, pp. 407–414
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2018
Cited alongside, same era.
S. Krishnamoorthy, “Sentiment analysis of financial news articles using performance indicators,” Knowl. Inf. Syst. , vol. 56, no. 2, p. 373–394, Aug. 2018. [Online]. Available: https://doi.org/10.1007/s10115-017-1134-1
2018
Cited alongside, same era.
S. Yadav, A. Ekbal, S. Saha, and P. Bhattacharyya, “Medical sentiment analysis using social media: Towards building a patient assisted system,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) . Miyazaki, Japan: European Language Resources Association (ELRA), May 2018. [Online]. Available: https://www.aclweb.org/anthology/L18-1442
2018
Cited alongside, same era.
Y. Zhang, Q. Liu, and L. Song, “Sentence-state lstm for text representation,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp. 317–327
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. Howard and S. Ruder, “Universal language model fine-tuning for text classification,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp. 328–339
2018
Cited alongside, same era.
S. Kiritchenko and S. Mohammad, “Examining gender and race bias in two hundred sentiment analysis systems,” in Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics , 2018, pp. 43–53
2018
Cited alongside, same era.
M. Díaz, I. Johnson, A. Lazar, A. M. Piper, and D. Gergle, “Addressing age-related bias in sentiment analysis,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems , 2018, pp. 1–14
2018
Cited alongside, same era.
M. Aniche, C. Treude, I. Steinmacher, I. Wiese, G. Pinto, M.-A. Storey, and M. A. Gerosa, “How modern news aggregators help development communities shape and share knowledge,” in Proceedings of the 40th International Conference on Software Engineering , ser. ICSE ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 499–510. [Online]. Available: https://doi.org/10.1145/3180155.3180180
2018
Cited alongside, same era.
2019
Later among the works it cites.
D. S. Sachan, M. Zaheer, and R. Salakhutdinov, “Revisiting lstm networks for semi-supervised text classification via mixed objective function,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 6940–6948
2019
Later among the works it cites.
Z. Q. Zhou and L. Sun, “Metamorphic testing of driverless cars,” Communications of the ACM , vol. 62, no. 3, pp. 61–67, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Chakraborty, S. Majumder, Z. Yu, and T. Menzies, “Fairway: a way to build fair ml software,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020, pp. 654–665
2020
Later among the works it cites.
M. T. Ribeiro, T. Wu, C. Guestrin, and S. Singh, “Beyond accuracy: Behavioral testing of nlp models with checklist,” Association for Computational Linguistics (ACL 2020 , 2020
2020
Later among the works it cites.
S. Poria, D. Hazarika, N. Majumder, and R. Mihalcea, “Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research,” IEEE Transactions on Affective Computing , 2020, accepted (early access at: https://ieeexplore.ieee.org/document/9260964)
2020
Later among the works it cites.
O. Oyebode, F. Alqahtani, and R. Orji, “Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews,” IEEE Access , vol. 8, pp. 111 141–111 158, 2020
2020
Later among the works it cites.
P. Ma, S. Wang, and J. Liu, “Metamorphic testing and certified mitigation of fairness violations in nlp models,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 , C. Bessiere, Ed. International Joint Conferences on Artificial Intelligence Organization, 7 2020, pp. 458–465, main track. [Online]. Available: https://doi.org/10.24963/ijcai.2020/64
2020
Later among the works it cites.
E. Soremekun, S. Udeshi, and S. Chattopadhyay, “Astraea: Grammar-based fairness testing,” 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “Albert: A lite bert for self-supervised learning of language representations,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=H1eA7AEtvS
2020
Later among the works it cites.
K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning, “Electra: Pre-training text encoders as discriminators rather than generators,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=r1xMH1BtvB
2020
Later among the works it cites.
J. M. Zhang, M. Harman, L. Ma, and Y. Liu, “Machine learning testing: Survey, landscapes and horizons,” IEEE Transactions on Software Engineering , 2020
2020
Later among the works it cites.
Z. Sun, J. Zhang, M. Harman, M. Papadakis, and L. Zhang, “Automatic testing and improvement of machine translation,” in International Conference on Software Engineering (ICSE) , 2020
2020
Later among the works it cites.
Y. Tay, M. Dehghani, J. P. Gupta, V. K. Aribandi, D. Bahri, Z. Qin, and D. Metzler, “Are pretrained convolutions better than pretrained transformers?” in ACL 2021 , 2021
2021
Closest in time.
A. Aghajanyan, A. Gupta, A. Shrivastava, X. Chen, L. Zettlemoyer, and S. Gupta, “Muppet: Massive multi-task representations with pre-finetuning,” 2021
2021
Closest in time.
W. Yuan, G. Neubig, and P. Liu, “Bartscore: Evaluating generated text as text generation,” in To be published in NeurIPS 2021 , 2021
2021
Closest in time.
A. R. Fabbri, W. Kryscinski, B. McCann, R. Socher, and D. Radev, “Summeval: Re-evaluating summarization evaluation,” Transactions of the Association for Computational Linguistics , vol. 9, pp. 391–409, 2021
2021
Closest in time.
J. E. Montandon, C. Politowski, L. L. Silva, M. T. Valente, F. Petrillo, and Y.-G. Guéhéneuc, “What skills do it companies look for in new developers? a study with stack overflow jobs,” Information and Software Technology , vol. 129, p. 106429, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0950584920301877
2021
Closest in time.
J. Wang, L. Li, and A. Zeller, “Restoring execution environments of jupyter notebooks,” in To be published in ICSE 2021 , 2021
2021
Closest in time.
Z. Yang, M. H. Asyrofi, and D. Lo, “Biasrv: Uncovering biased sentiment predictions at runtime,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , ser. ESEC/FSE 2021. New York, NY, USA: Association for Computing Machinery, 2021, p. 1540–1544. [Online]. Available: https://doi.org/10.1145/3468264.3473117
2021
Closest in time.
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2021
Closest in time.