This work is licensed under the Creative Commons Attribution 4.0 International License.
Barros, P., Parisi, G. I., Weber, C., & Wermter, S. (2017). Emotion-modulated attention improves expression recognition: a deep learning model. Neurocomputing, 253(Aug.30), 104-114.Search in Google Scholar
Habernal, I., Ptacek, T., & Steinberger, J. (2015). Supervised sentiment analysis in czech social media. Information Processing & Management, 51(4), 532-546.Search in Google Scholar
Al-Moslmi, T., Albared, M., Al-Shabi, A., Omar, N., & Abdullah, S. (2017). Arabic senti-lexicon: constructing publicly available language resources for arabic sentiment analysis. Journal of Information Science, 016555151668390.Search in Google Scholar
Vilares, D., Alonso, M. A., & Gómez-Rodríguez, Carlos. (2017). Supervised sentiment analysis in multilingual environments. Information Processing & Management, 53(3), 595-607.Search in Google Scholar
Liu, S., & Liu, J. (2021). Public attitudes toward covid-19 vaccines on english-language twitter: a sentiment analysis: Vaccine(1).Search in Google Scholar
Ghasemi, R., Asli, S. A. A., & Momtazi, S. (2022). Deep persian sentiment analysis: cross-lingual training for low-resource languages. Journal of Information Science, 48(4), 449-462.Search in Google Scholar
Ahmed, Q. L. Z. (2020). Constructing domain-dependent sentiment dictionary for sentiment analysis. Neural computing & applications, 32(18).Search in Google Scholar
Phu, V., N., Tran, & V., T. N. (2018). Latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system. International journal of computers, communications and control.Search in Google Scholar
Rani, S., & Kumar, P. (2017). A sentiment analysis system to improve teaching and learning. Computer, 50(5), 36-43.Search in Google Scholar
Barnes, J., & Klinger, R. (2019). Embedding projection for targeted cross-lingual sentiment: model comparisons and a real-world study. Journal of Artificial Intelligence Research, 66.Search in Google Scholar
Pota, M., Ventura, M., Fujita, H., & Esposito, M. (2021). Multilingual evaluation of pre-processing for bert-based sentiment analysis of tweets. Expert Systems with Applications, 115119.Search in Google Scholar
Matheus Araújo a b, B, A. P., & Fabrício Benevenuto b. (2020). A comparative study of machine translation for multilingual sentence-level sentiment analysis. Information Sciences, 512, 1078-1102.Search in Google Scholar
Chakravarthi, B. R., Muralidaran, V., Priyadharshini, R., & McCrae, J. P. (2020). Corpus creation for sentiment analysis in code-mixed Tamil-English text. arXiv preprint arXiv:2006.00206.Search in Google Scholar
Araujo, M., Reis, J., Pereira, A., & Benevenuto, F. (2016, April). An evaluation of machine translation for multilingual sentence-level sentiment analysis. In Proceedings of the 31st annual ACM symposium on applied computing(pp. 1140-1145).Search in Google Scholar
Can, E. F., Ezen-Can, A., & Can, F. (2018). Multilingual sentiment analysis: An RNN-based framework for limited data.arXiv preprint arXiv: 1806.04511.Search in Google Scholar
Hyun, D., Cho, J., & Yu, H. (2020, December). Building large-scale English and Korean datasets for aspect-level sentiment analysis in automotive domain. In Proceedings of the 28th international conference on computational linguistics (pp. 961-966).Search in Google Scholar
Korayem, M., Aljadda, K., & Crandall, D. (2016). Sentiment/subjectivity analysis survey for languages other than English.Social network analysis and mining, 6, 1-17.Search in Google Scholar