Abstract
We developed and validated a language-agnostic method for sentiment analysis. Cross-language experiments carried out on the new MultiEmo dataset with texts in 11 languages proved that LaBSE embeddings with an additional attention layer implemented in the BiLSTM architecture outperformed other methods in most cases.
This work was partially supported by the National Science Centre, Poland, project no. 2020/37/B/ST6/03806; by the statutory funds of the Department of Artificial Intelligence, Wroclaw University of Science and Technology; by the European Regional Development Fund as a part of the 2014-2020 Smart Growth Operational Programme, CLARIN - Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.
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References
Hemmatian, F., Sohrabi, M.K.: A survey on classification techniques for opinion mining and sentiment analysis. Artif. Intell. Rev.
Augustyniak, Ł., Szymański, P., Kajdanowicz, T., Kazienko, P.: Fast and accurate-improving lexicon-based sentiment classification with an ensemble methods
Bartusiak, R., Augustyniak, L., Kajdanowicz, T., Kazienko, P.: Sentiment analysis for polish using transfer learning approach. In: ENIC 2015 (2015)
Miłkowski, P., Gruza, M., Kanclerz, K., Kazienko, P., Grimling, D., Kocon, J.: Personal bias in prediction of emotions elicited by textual opinions. In: ACL-IJCNLP, Student Research Workshop. ACL, vol. 2021, pp. 248–259 (2021)
Kocoń, J., et al.: Learning personal human biases and representations for subjective tasks in natural language processing. In: ICDM, vol. 2021, pp. 1168–1173. IEEE (2021)
Artetxe, M., Schwenk, H.: Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond. Trans. ACL
Feng, F., Yang, Y., Cer, D., Arivazhagan, N., Wang, W.: Language-agnostic BERT sentence embedding. arXiv preprint arXiv:2007.01852 (2020)
Kanclerz, K., Miłkowski, P., Kocoń, J.: Cross-lingual deep neural transfer learning in sentiment analysis. Procedia Comput. Sci. 176, 128–137 (2020)
Chen, T., Xu, R., He, Y., Wang, X.: Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst. Appl.
Kocoń, J., Miłkowski, P., Zaśko-Zielińska, M.: Multi-level sentiment analysis of PolEmo 2.0: extended corpus of multi-domain consumer reviews. In: CoNLL 2019 (2019)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach
Rybak, P., Mroczkowski, R., Tracz, J., Gawlik, I.: KLEJ: comprehensive benchmark for polish language understanding. arXiv preprint arXiv:2005.00630 (2020)
Calais Guerra, P.H., Veloso, A., Meira Jr, W., Almeida, V.: From bias to opinion: a transfer-learning approach to real-time sentiment analysis. In: ACM SIGKDD 2011 (2011)
Pelicon, A., Pranjić, M., Miljković, D., Škrlj, B., Pollak, S.: Zero-shot learning for cross-lingual news sentiment classification. Appl. Sci. 10(17), 5993 (2020)
Zhou, X., Wan, X., Xiao, J.: Attention-based LSTM network for cross-lingual sentiment classification. In: EMNLP 2016, pp. 247–256 (2016)
Pires, T., Schlinger, E., Garrette, D.: How multilingual is multilingual BERT? In: Proceedings of the 57th Annual Meeting of the ACL, 2019, pp. 4996–5001 (2019)
Shen, L., Xu, J., Weischedel, R.: A new string-to-dependency machine translation algorithm with a target dependency language model. In: ACL-08: HLT
Guo, M., et al.: Effective parallel corpus mining using bilingual sentence embeddings
Yang, Y., et al.: Improving multilingual sentence embedding using bi-directional dual encoder with additive margin softmax. arXiv preprint arXiv:1902.08564 (2019)
Gawron, K., Pogoda, M., Ropiak, N., Swędrowski, M., Kocoń, J.: Deep neural language-agnostic multi-task text classifier. In: ICDM 2021, pp. 136–142. IEEE (2021)
Hripcsak, G., Rothschild, A.S.: Agreement, the f-measure, and reliability in information retrieval. JAMIA 12(3), 296–298 (2005)
Kocoń, J., Figas, A., Gruza, M., Puchalska, D., Kajdanowicz, T., Kazienko, P.: Offensive, aggressive, and hate speech analysis: from data-centric to human-centered approach. Inf. Process. Manag. 58(5), 102643 (2021)
Kanclerz, K., et al.: Controversy and conformity: from generalized to personalized aggressiveness detection. In: ACL-IJCNLP. ACL, vol. 2021, pp. 5915–5926 (2021)
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Miłkowski, P., Gruza, M., Kazienko, P., Szołomicka, J., Woźniak, S., Kocoń, J. (2022). MultiEmo: Language-Agnostic Sentiment Analysis. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_10
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