{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T09:32:28Z","timestamp":1759483948603,"version":"3.37.3"},"reference-count":96,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["311275\/2020-6"],"award-info":[{"award-number":["311275\/2020-6"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"publisher","award":["E26\/202.914\/2019 (247109)","310444\/2018-7"],"award-info":[{"award-number":["E26\/202.914\/2019 (247109)","310444\/2018-7"]}],"id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"publisher","award":["SEI-260003\/000614\/2023","E-26\/201.139\/2022"],"award-info":[{"award-number":["SEI-260003\/000614\/2023","E-26\/201.139\/2022"]}],"id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Lang Resources & Evaluation"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s10579-023-09661-4","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T07:02:21Z","timestamp":1687935741000},"page":"223-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Sentiment analysis in Portuguese tweets: an evaluation of diverse word representation models"],"prefix":"10.1007","volume":"58","author":[{"given":"Daniela","family":"Vianna","sequence":"first","affiliation":[]},{"given":"Fernando","family":"Carneiro","sequence":"additional","affiliation":[]},{"given":"Jonnathan","family":"Carvalho","sequence":"additional","affiliation":[]},{"given":"Alexandre","family":"Plastino","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9089-7303","authenticated-orcid":false,"given":"Aline","family":"Paes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"9661_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107373","volume":"107","author":"MM Ag\u00fcero-Torales","year":"2021","unstructured":"Ag\u00fcero-Torales, M. M., Salas, J. I. A., & L\u00f3pez-Herrera, A. G. (2021). Deep learning and multilingual sentiment analysis on social media data: An overview. Applied Soft Computing, 107, 107373.","journal-title":"Applied Soft Computing"},{"key":"9661_CR3","doi-asserted-by":"crossref","unstructured":"Alves, A. L., Baptista, C. D. S., Andrade, L. H. D., & Paes, R. (2015). Uso de t\u00e9cnicas de an\u00e1lise de sentimentos em tweets relacionados ao meio-ambiente. In Anais do Workshop de Computa\u00e7\u00e3o Aplicada \u00e0 Gest\u00e3o do Meio Ambiente e Recursos Naturais (WCAMA), 2015 (pp. 37\u201346). Sociedade Brasileira de Computacao.","DOI":"10.5753\/wcama.2015.10188"},{"key":"9661_CR4","unstructured":"Alves, A. L., Baptista, C. D. S., Firmino, A. A., Oliveira, M. G. D., & Paiva, A. C. D. (2014). A comparison of SVM versus Naive-Bayes techniques for sentiment analysis in tweets: A case study with the 2013 FIFA confederations cup. In WebMedia 2014\u2014Proceedings of the 20th Brazilian symposium on multimedia and the web, 2014 (pp. 123\u2013130). Association for Computing Machinery, Inc."},{"key":"9661_CR6","first-page":"17","volume":"10","author":"G Ara\u00fajo","year":"2018","unstructured":"Ara\u00fajo, G., Teixeira, F., Mancini, F., Guimar\u00e3es, M., & Pisa, I. (2018). Sentiment analysis of Twitter\u2019s health messages in Brazilian Portuguese. Journal of Health Informatics, 10, 17\u201324.","journal-title":"Journal of Health Informatics"},{"key":"9661_CR7","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1016\/j.ins.2019.10.031","volume":"512","author":"M Ara\u00fajo","year":"2020","unstructured":"Ara\u00fajo, M., Pereira, A., & Benevenuto, F. (2020). A comparative study of machine translation for multilingual sentence-level sentiment analysis. Information Sciences, 512, 1078\u20131102.","journal-title":"Information Sciences"},{"key":"9661_CR5","doi-asserted-by":"crossref","unstructured":"Araujo, M., Pereira, A., Reis, J., & Benevenuto, F. (2016). An evaluation of machine translation for multilingual sentence-level sentiment analysis. In Proceedings of the ACM symposium on applied computing, 2016, August 4 (pp. 1140\u20131145). Association for Computing Machinery.","DOI":"10.1145\/2851613.2851817"},{"key":"9661_CR8","doi-asserted-by":"publisher","unstructured":"Barreto, S., Moura, R., Carvalho, J., Paes, A., & Plastino, A. (2021). Sentiment analysis in tweets: an assessment study from classical to modern text representation models. Data Min Knowl Disc, 37, 318\u2013380 (2023). https:\/\/doi.org\/10.1007\/s10618-022-00853-0","DOI":"10.1007\/s10618-022-00853-0"},{"key":"9661_CR10","doi-asserted-by":"crossref","unstructured":"Belis\u00e1rio, L. B., Ferreira, L. G., & Pardo, T. A. S. (2020). Evaluating methods of different paradigms for subjectivity classification in Portuguese. In Proceedings of the 14th international conference on the computational processing of Portuguese, LNAI, 2020 (Vol. 12037, pp. 261\u2013269). Springer.","DOI":"10.1007\/978-3-030-41505-1_25"},{"key":"9661_CR11","doi-asserted-by":"publisher","unstructured":"Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In: M. C. Elish, W. Isaac & R. S. Zemel (Eds.), FAccT \u201921: 2021 ACM conference on fairness, accountability, and transparency, virtual event, Toronto, Canada, March 3\u201310, 2021 (pp. 610\u2013623). ACM. https:\/\/doi.org\/10.1145\/3442188.3445922.","DOI":"10.1145\/3442188.3445922"},{"key":"9661_CR12","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135\u2013146.","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"9661_CR13","unstructured":"Brum, H. B., & das Gra\u00e7as Volpe Nunes, M. (2018). Building a sentiment corpus of tweets in Brazilian Portuguese. In N. C. C. (chair), K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis & T. Tokunaga (Eds.), Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018), 2018. European Language Resources Association (ELRA)."},{"key":"9661_CR14","doi-asserted-by":"crossref","unstructured":"Brum, H. B., & Nunes, M. D. G. V. (2018). Semi-supervised sentiment annotation of large corpora. In Proceedings of the 13th international conference on the computational processing of Portuguese, 2018 (pp. 385\u2013395). Springer.","DOI":"10.1007\/978-3-319-99722-3_39"},{"key":"9661_CR15","unstructured":"Carmo, D., Piau, M., Campiotti, I., Nogueira, R., & Lotufo, R. (2020). PTT5: Pretraining and validating the t5 model on Brazilian Portuguese data. arXiv preprint. arXiv:2008.09144"},{"key":"9661_CR16","unstructured":"Carosia, A., Coelho, G. P., & da Silva, A. E. A. (2019). The influence of tweets and news on the Brazilian Stock Market through sentiment analysis. In Proceedings of the 25th Brazilian symposium on multimedia and the web, 2019. ACM."},{"key":"9661_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/08839514.2019.1673037","volume":"34","author":"A Carosia","year":"2020","unstructured":"Carosia, A., Coelho, G. P., & Silva, A. E. A.  (2020). Analyzing the Brazilian financial market through Portuguese sentiment analysis in social media. Applied Artificial Intelligence, 34, 1\u201319.","journal-title":"Applied Artificial Intelligence"},{"issue":"3","key":"9661_CR18","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1007\/s10462-020-09895-6","volume":"54","author":"J Carvalho","year":"2021","unstructured":"Carvalho, J., & Plastino, A. (2021). On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis. Artificial Intelligence Review, 54(3), 1887\u20131936.","journal-title":"Artificial Intelligence Review"},{"key":"9661_CR19","doi-asserted-by":"publisher","DOI":"10.5617\/osla.1444","author":"P Carvalho","year":"2015","unstructured":"Carvalho, P., & Silva, M. J. (2015). Sentilex-pt: Principais caracter\u00edsticas e potencialidades. Oslo Studies in Language. https:\/\/doi.org\/10.5617\/osla.1444","journal-title":"Oslo Studies in Language"},{"key":"9661_CR21","doi-asserted-by":"crossref","unstructured":"Chan, B., Schweter, S., & M\u00f6ller, T. (2020). German\u2019s next language model. In Proceedings of the 28th International Conference on Computational Linguistics, 2020 (pp. 6788\u20136796). International Committee on Computational Linguistics (Online).","DOI":"10.18653\/v1\/2020.coling-main.598"},{"key":"9661_CR22","doi-asserted-by":"crossref","unstructured":"Correa, E. A., Marinho, V. Q., Santos, L. B. D., Bertaglia, T. F. C., Treviso, M. V., & Brum, H. B. (2017). PELESent: Cross-domain polarity classification using distant supervision. In Proceedings\u20142017 Brazilian conference on intelligent systems, BRACIS 2017, 2017, January 2018 (pp. 49\u201354). Institute of Electrical and Electronics Engineers, Inc.","DOI":"10.1109\/BRACIS.2017.45"},{"key":"9661_CR23","doi-asserted-by":"crossref","unstructured":"Costa, J. M. R., Rotabi, R., Murnane, E. L., & Choudhury, T. (2015). It is not only about grievances: Emotional dynamics in social media during the Brazilian protests. In Proceedings of the international AAAI conference on web and social media, 2015 (Vol. 9).","DOI":"10.1609\/icwsm.v9i1.14667"},{"key":"9661_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0208-1","volume":"6","author":"RM Cury","year":"2019","unstructured":"Cury, R. M. (2019). Oscillation of tweet sentiments in the election of Jo\u00e3o Doria Jr. for Mayor. Journal of Big Data, 6, 1\u201315.","journal-title":"Journal of Big Data"},{"key":"9661_CR73","doi-asserted-by":"crossref","unstructured":"da Silva, A. M., Bastos, R. D. M., & de Azevedo da Rocha, R. L. (2018). Sentiment analysis in Brazilian Portuguese tweets in the domain of calamity: Application of the summarization method and semantic similarity in polarized terms. In IJCCI 2018\u2014Proceedings of the 10th international joint conference on computational intelligence, 2018 (pp. 225\u2013231). SciTe Press.","DOI":"10.5220\/0006947802250231"},{"key":"9661_CR2","doi-asserted-by":"crossref","unstructured":"De Aguiar, E. J., Fai\u00e7al, B. S., Ueyama, J., Silva, G. C., & Menolli, A. (2018). An\u00e1lise de sentimento em redes sociais para a l\u00edngua portuguesa utilizando algoritmos de classifica\u00e7\u00e3o. In Anais do XXXVI Simp\u00f3sio Brasileiro de Redes de Computadores e Sistemas Distribu\u00eddos. SBC.","DOI":"10.5753\/sbrc.2018.2430"},{"key":"9661_CR9","doi-asserted-by":"crossref","unstructured":"De Barros, T. M., Pedrini, H., & Dias, Z. (2021). Leveraging emoji to improve sentiment classification of tweets. In Proceedings of the 36th annual ACM symposium on applied computing, 2021 (pp. 845\u2013852). ACM.","DOI":"10.1145\/3412841.3441960"},{"key":"9661_CR20","doi-asserted-by":"crossref","unstructured":"de Carvalho, V. D. H., Nepomuceno, T. C. C., & Costa, A. P. C. S. (2020). An automated corpus annotation experiment in Brazilian Portuguese for sentiment analysis in public security. In Lecture notes in business information processing, LNBIP (Vol. 384 pp. 99\u2013111). Springer.","DOI":"10.1007\/978-3-030-46224-6_8"},{"key":"9661_CR50","doi-asserted-by":"publisher","DOI":"10.2196\/24585","volume":"7","author":"T de Melo","year":"2021","unstructured":"de Melo, T., & Figueiredo, C. M. (2021). Comparing news articles and tweets about COVID-19 in Brazil: Sentiment analysis and topic modeling approach. JMIR Public Health and Surveillance, 7, e24585.","journal-title":"JMIR Public Health and Surveillance"},{"key":"9661_CR62","doi-asserted-by":"publisher","first-page":"15391","DOI":"10.1007\/s11042-020-10323-8","volume":"80","author":"DN de Oliveira","year":"2021","unstructured":"de Oliveira, D. N., & de Campos Merschmann, L. H. (2021). Joint evaluation of preprocessing tasks with classifiers for sentiment analysis in Brazilian Portuguese language. Multimedia Tools and Applications, 80, 15391\u201315412.","journal-title":"Multimedia Tools and Applications"},{"issue":"2","key":"9661_CR80","doi-asserted-by":"publisher","first-page":"79","DOI":"10.5752\/P.2316-9451.2017v5n2p79","volume":"5","author":"KF de Souza","year":"2017","unstructured":"de Souza, K. F., Pereira, M. H. R., & Dalip, D. H. (2017). Unilex: M\u00e9todo l\u00e9xico para an\u00e1lise de sentimentos textuais sobre conte\u00fado de tweets em portugu\u00eas brasileiro. Abak\u00f3s, 5(2), 79\u201396.","journal-title":"Abak\u00f3s"},{"key":"9661_CR88","unstructured":"de Vargas Feij\u00f3, D., & Moreira, V. P. (2020). Mono vs. multilingual transformer-based models: A comparison across several language tasks. CoRR abs\/2007.09757. arxiv:2007.09757"},{"key":"9661_CR25","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In J. Burstein, C. Doran & T. Solorio (Eds.), Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019: Long and Short Papers, Minneapolis, MN, USA, June 2\u20137, 2019. (Vol.1, pp. 4171\u20134186). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/n19-1423.","DOI":"10.18653\/v1\/n19-1423"},{"key":"9661_CR70","doi-asserted-by":"crossref","unstructured":"dos Santos, A., J\u00fanior, J. D. B., & de Arruda Camargo, H. (2018). Annotation of a corpus of tweets for sentiment analysis. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), LNAI (Vol. 11122, pp. 294\u2013302). Springer.","DOI":"10.1007\/978-3-319-99722-3_30"},{"key":"9661_CR26","unstructured":"Filho, J. A. W., Wilkens, R., Idiart, M., & Villavicencio, A. (2018). The brWaC corpus: A new open resource for Brazilian Portuguese. In Proceedings of the eleventh international conference on language resources and evaluation, LREC 2018, Miyazaki, Japan, May 7\u201312, 2018. European Language Resources Association (ELRA). http:\/\/www.lrec-conf.org\/proceedings\/lrec2018\/summaries\/599.html"},{"key":"9661_CR27","unstructured":"Fran\u00e7a, T., & Oliveira, J. (2014). An\u00e1lise de sentimento de tweets relacionados aos protestos que ocorreram no brasil entre junho e agosto de 2013. In Anais do III Brazilian workshop on social network analysis and mining, 2014 (pp. 128\u2013139). SBC."},{"key":"9661_CR28","first-page":"23","volume":"12","author":"P Gage","year":"1994","unstructured":"Gage, P. (1994). A new algorithm for data compression. The C Users Journal Archive, 12, 23\u201338.","journal-title":"The C Users Journal Archive"},{"key":"9661_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.107057","volume":"101","author":"K Garcia","year":"2021","unstructured":"Garcia, K., & Berton, L. (2021). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101, 107057.","journal-title":"Applied Soft Computing"},{"key":"9661_CR30","doi-asserted-by":"crossref","unstructured":"Gengo, P., & Verri, F. A. (2020). Semi-supervised sentiment analysis of Portuguese tweets with random walk in feature sample networks. In Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics), LNAI (Vol. 12319, pp. 595\u2013605). Springer.","DOI":"10.1007\/978-3-030-61377-8_42"},{"key":"9661_CR31","doi-asserted-by":"publisher","unstructured":"Ghojogh, B., Crowley, M., Karray, F., & Ghodsi, A. (2023). Uniform manifold approximation and projection (UMAP) (pp. 479\u2013497). Springer. https:\/\/doi.org\/10.1007\/978-3-031-10602-6.","DOI":"10.1007\/978-3-031-10602-6"},{"key":"9661_CR32","doi-asserted-by":"crossref","unstructured":"Gomes, F. B., Ad\u00e1n-Coello, J. M., & Kintschner, F. E. (2018). Studying the effects of text preprocessing and ensemble methods on sentiment analysis of Brazilian Portuguese tweets. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), LNAI (Vol. 11171, pp. 167\u2013177). Springer.","DOI":"10.1007\/978-3-030-00810-9_15"},{"key":"9661_CR33","doi-asserted-by":"publisher","first-page":"3467","DOI":"10.1109\/TLA.2016.7587656","volume":"14","author":"P Grandin","year":"2016","unstructured":"Grandin, P., & Adan, J. M. (2016). Piegas: A systems for sentiment analysis of tweets in Portuguese. IEEE Latin America Transactions, 14, 3467\u20133473.","journal-title":"IEEE Latin America Transactions"},{"key":"9661_CR34","doi-asserted-by":"crossref","unstructured":"Guerra, P. H. C., Meira, W., & Cardie, C. (2014). Sentiment analysis on evolving social streams: How self-report imbalances can help. In WSDM 2014\u2014Proceedings of the 7th ACM international conference on web search and data mining (pp. 443\u2013452). Association for Computing Machinery.","DOI":"10.1145\/2556195.2556261"},{"key":"9661_CR35","unstructured":"Guerra, P. H. C., Veloso, A., Meira, W., & Almeida, V. (2011). From bias to opinion: A transfer-learning approach to real-time sentiment analysis. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining\u2014KDD \u201911, 2011. ACM Press."},{"key":"9661_CR36","doi-asserted-by":"crossref","unstructured":"Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don\u2019t stop pretraining: Adapt language models to domains and tasks. In Proceedings of the 58th annual meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5\u201310, 2020 (pp. 8342\u20138360). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.acl-main.740"},{"key":"9661_CR37","unstructured":"Heinzerling, B., & Strube, M. (2018). BPEmb: Tokenization-free pre-trained subword embeddings in 275 languages. In N. Calzolari, K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis & T. Tokunaga (Eds.), Proceedings of the eleventh international conference on language resources and evaluation, LREC 2018, Miyazaki, Japan, May 7\u201312, 2018. European Language Resources Association (ELRA)."},{"key":"9661_CR38","doi-asserted-by":"crossref","unstructured":"Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media, 2014. The AAAI Press.","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"9661_CR39","doi-asserted-by":"crossref","unstructured":"Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter sentiment analysis: The good the bad and the omg! In Proceedings of the international AAAI conference on web and social media, 2011 (Vol. 5).","DOI":"10.1609\/icwsm.v5i1.14185"},{"key":"9661_CR40","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2020). ALBERT: A lite BERT for self-supervised learning of language representations. In 8th International conference on learning representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=H1eA7AEtvS"},{"issue":"3","key":"9661_CR41","first-page":"56","volume":"7","author":"BP Lauand","year":"2014","unstructured":"Lauand, B. P., & Oliveira, J. (2014). Inferindo as condi\u00e7\u014des de tr\u00e2nsito atrav\u00e9s da an\u00e1lise de sentimentos no Twitter. iSys - Revista Brasileira de Sistemas de Informa\u00e7\u00e3o, 7(3), 56\u201374.","journal-title":"iSys - Revista Brasileira de Sistemas de Informa\u00e7\u00e3o"},{"key":"9661_CR42","doi-asserted-by":"publisher","DOI":"10.5121\/ijaia.2016.7106","author":"ML Lima","year":"2016","unstructured":"Lima, M. L., Nascimento, T. P., Labidi, S., Timb\u00f3, N. S., Batista, M. V. L., Neto, G. N., Costa, E. A. M., & Sousa, S. R. S. (2016). Using sentiment analysis for stock exchange prediction. International Journal of Artificial Intelligence and Applications. https:\/\/doi.org\/10.5121\/ijaia.2016.7106","journal-title":"International Journal of Artificial Intelligence and Applications"},{"key":"9661_CR43","doi-asserted-by":"publisher","DOI":"10.1017\/9781108639286","volume-title":"Sentiment analysis: Mining opinions, sentiments, and emoticons","author":"B Liu","year":"2020","unstructured":"Liu, B. (2020). Sentiment analysis: Mining opinions, sentiments, and emoticons. Cambridge University Press."},{"key":"9661_CR44","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. CoRR abs\/1907.11692. arxiv:1907.11692"},{"key":"9661_CR45","doi-asserted-by":"crossref","unstructured":"Lourenco Jr., R., Veloso, A., Pereira, A., Meira Jr., W., Ferreira, R., & Parthasarathy, S. (2014). Economically-efficient sentiment stream analysis. In Proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, SIGIR \u201914, 2014 (pp. 637\u2013646). Association for Computing Machinery.","DOI":"10.1145\/2600428.2609612"},{"key":"9661_CR46","doi-asserted-by":"crossref","unstructured":"Machado, M. T., Pardo, T. A. S., & Ruiz, E. E. S. (2018). Creating a Portuguese context sensitive lexicon for sentiment analysis. In Computational processing of the Portuguese language\u201413th International conference, PROPOR 2018, Canela, Brazil, September 24\u201326, 2018, Proceedings, lecture notes in computer science (vol. 11122, pp. 335\u2013344). Springer.","DOI":"10.1007\/978-3-319-99722-3_34"},{"key":"9661_CR47","doi-asserted-by":"publisher","first-page":"323","DOI":"10.18617\/liinc.v13i2.4089","volume":"13","author":"F Malini","year":"2017","unstructured":"Malini, F., Ciarelli, P., & Medeiros, J. (2017). O sentimento pol\u00edtico em redes sociais: big data, algoritmos e as emo\u00e7\u014des nos tweets sobre o impeachment de dilma rousseff. Liinc em Revista, 13, 323\u2013342.","journal-title":"Liinc em Revista"},{"key":"9661_CR48","doi-asserted-by":"crossref","unstructured":"Martin, L., M\u00fcller, B., Su\u00e1rez, P.J.O., Dupont, Y., Romary, L., de la Clergerie, \u00c9., Seddah, D., & Sagot, B. (2020). CamemBERT: A tasty French language model. In Proceedings of the 58th annual meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5\u201310, 2020 (pp. 7203\u20137219). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.acl-main.645"},{"key":"9661_CR49","doi-asserted-by":"crossref","unstructured":"Martins, R., Pereira, A., & Benevenuto, F. (2015). An approach to sentiment analysis of web applications in Portuguese. In Proceedings of the 21st Brazilian symposium on multimedia and the web, WebMedia \u201915, 2015 (pp. 105\u2013112). Association for Computing Machinery.","DOI":"10.1145\/2820426.2820446"},{"key":"9661_CR51","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th international conference on neural information processing systems, NIPS\u201913, 2013 (Vol. 2, pp. 3111\u20133119)."},{"issue":"3","key":"9661_CR52","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1111\/j.1467-8640.2012.00460.x","volume":"29","author":"SM Mohammad","year":"2013","unstructured":"Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word\u2013emotion association lexicon. Computational Intelligence, 29(3), 436\u2013465.","journal-title":"Computational Intelligence"},{"key":"9661_CR53","doi-asserted-by":"crossref","unstructured":"Moraes, S. M., Santos, A. L., Redecker, M., Machado, R. M., & Meneguzzi, F. R. (2016). Comparing approaches to subjectivity classification: A study on Portuguese tweets. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 9727, pp. 86\u201394). Springer.","DOI":"10.1007\/978-3-319-41552-9_8"},{"key":"9661_CR54","doi-asserted-by":"crossref","unstructured":"Nankani, H., Dutta, H., Shrivastava, H., Krishna, P. R., Mahata, D., & Shah, R. R. (2020). Multilingual sentiment analysis. In Deep learning-based approaches for sentiment analysis (pp. 193\u2013236). Springer.","DOI":"10.1007\/978-981-15-1216-2_8"},{"key":"9661_CR55","doi-asserted-by":"publisher","first-page":"2","DOI":"10.21529\/RESI.2015.1402002","volume":"14","author":"P Nascimento","year":"2015","unstructured":"Nascimento, P., Osiek, B., & Xex\u00e9o, G. (2015). An\u00e1lise de sentimento de tweets com foco em not\u00edcias. Revista Eletr\u00f4nica de Sistemas de Informa\u00e7\u00e3o, 14, 2.","journal-title":"Revista Eletr\u00f4nica de Sistemas de Informa\u00e7\u00e3o"},{"key":"9661_CR56","doi-asserted-by":"crossref","unstructured":"Neuenschwander, B., Pereira, A., Meira, W., & Barbosa, D. (2014). Sentiment analysis for streams of web data: A case study of Brazilian financial markets. In WebMedia 2014\u2014Proceedings of the 20th Brazilian symposium on multimedia and the web, 2014 (pp. 167\u2013170). Association for Computing Machinery, Inc.","DOI":"10.1145\/2664551.2664579"},{"key":"9661_CR57","doi-asserted-by":"crossref","unstructured":"Nguyen, D. Q., Vu, T., & Nguyen, A. T. (2020). BERTweet: A pre-trained language model for English Tweets. In Proceedings of the 2020 conference on empirical methods in natural language processing: System demonstrations, 2020 (pp. 9\u201314).","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"9661_CR58","unstructured":"Nozza, D., Bianchi, F., & Hovy, D. (2020). What the [mask]? Making sense of language-specific BERT models. CoRR abs\/2003.02912. arxiv:2003.02912"},{"key":"9661_CR60","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1590\/1984-9240827","volume":"24","author":"DJS Oliveira","year":"2017","unstructured":"Oliveira, D. J. S., & de Souza Bermejo, P. H. (2017). M\u00eddias sociais e administra\u00e7\u00e3o p\u00fablica: an\u00e1lise do sentimento social perante a atua\u00e7\u00e3o do governo federal brasileiro. Organiza\u00e7\u014des & Sociedade, 24, 491\u2013508.","journal-title":"Organiza\u00e7\u014des & Sociedade"},{"key":"9661_CR61","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1080\/19331681.2016.1214094","volume":"14","author":"DJS Oliveira","year":"2017","unstructured":"Oliveira, D. J. S., de Souza Bermejo, P. H., & dos Santos, P. A. (2017). Can social media reveal the preferences of voters? A comparison between sentiment analysis and traditional opinion polls. Journal of Information Technology and Politics, 14, 34\u201345.","journal-title":"Journal of Information Technology and Politics"},{"key":"9661_CR59","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1590\/0034-7612174204","volume":"53","author":"DJS Oliveira","year":"2019","unstructured":"Oliveira, D. J. S., Souza Bermejo, P. H., Pereira, J. R., & Barbosa, D. A. (2019). The application of the sentiment analysis technique in social media as a tool for social management practices at the governmental level. Revista de Administracao Publica, 53, 235\u2013251.","journal-title":"Revista de Administracao Publica"},{"key":"9661_CR63","doi-asserted-by":"crossref","unstructured":"Pasqualotti, P. R., & Vieira, R. (2008). Wordnetaffectbr: uma base lexical de palavras de emo\u00e7\u00f5es para a l\u00edngua portuguesa. RENOTE-Revista Novas Tecnologias na Educa\u00e7\u00e3o, 6(1).","DOI":"10.22456\/1679-1916.14693"},{"key":"9661_CR64","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825\u20132830.","journal-title":"Journal of Machine Learning Research"},{"key":"9661_CR65","doi-asserted-by":"publisher","unstructured":"Pennington, J., Socher, R., & Manning, C. (2014) GloVe: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014 (pp. 1532\u20131543). Association for Computational Linguistics. https:\/\/doi.org\/10.3115\/v1\/D14-1162.","DOI":"10.3115\/v1\/D14-1162"},{"key":"9661_CR66","first-page":"2020","volume":"5","author":"GRG Pessanha","year":"2020","unstructured":"Pessanha, G. R. G., Fidelis, T. O., Freire, C. D., & Soares, E. A. (2020). Fiqueemcasa: An\u00e1lise de sentimento dos usu\u00e1rios do twitter em rela\u00e7\u00e3o ao covid19. HOLOS, 5, 2020.","journal-title":"HOLOS"},{"key":"9661_CR67","unstructured":"Praciano, B. J. G., da Costa, J. P. C. L., Maranhao, J. P. A., de Mendon\u00e7a, F. L. L., de Sousa Junior, R. T., & Prettz, J. B. (2018). Spatio-temporal trend analysis of the Brazilian elections based on Twitter data. In IEEE international conference on data mining workshops, November 2018 (pp. 1355\u20131360). IEEE Computer Society."},{"key":"9661_CR68","doi-asserted-by":"crossref","unstructured":"Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In K. Inui, J. Jiang, V. Ng & X. Wan (Eds.), Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3\u20137, 2019 (pp. 3980\u20133990). Association for Computational Linguistics.","DOI":"10.18653\/v1\/D19-1410"},{"key":"9661_CR69","doi-asserted-by":"crossref","unstructured":"Rosa, R. L., Rodriguez, D. Z., & Bressan, G. (2013). SentiMeter-Br: A social web analysis tool to discover consumers\u2019 sentiment. In Proceedings\u2014IEEE international conference on mobile data management, 2013 (Vol. 2, pp. 122\u2013124).","DOI":"10.1109\/ISCE.2013.6570158"},{"key":"9661_CR71","doi-asserted-by":"crossref","unstructured":"Schuster, M., & Nakajima, K. (2012). Japanese and Korean voice search. In 2012 IEEE international conference on acoustics, speech and signal processing, ICASSP 2012, Kyoto, Japan, March 25\u201330, 2012 (pp. 5149\u20135152). IEEE.","DOI":"10.1109\/ICASSP.2012.6289079"},{"key":"9661_CR72","doi-asserted-by":"crossref","unstructured":"Severyn, A., & Moschitti, A. (2015). Twitter sentiment analysis with deep convolutional neural networks. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, 2015 (pp. 959\u2013962).","DOI":"10.1145\/2766462.2767830"},{"key":"9661_CR74","doi-asserted-by":"crossref","unstructured":"Silva, A. N. D., Souza, O. D., & Souza, J. N. D. (2020). Sentiment parser based on x-bar theory to Brazilian Portuguese. In Proceedings of the 2020 international conference on computing, electronics and communications engineering, 2020 (pp. 166\u2013171). Institute of Electrical and Electronics Engineers, Inc.","DOI":"10.1109\/iCCECE49321.2020.9231145"},{"key":"9661_CR75","doi-asserted-by":"crossref","unstructured":"Silva, I. S., Gomide, J., Veloso, A., Meira, W., & Ferreira, R. (2011). Effective sentiment stream analysis with self-augmenting training and demand-driven projection. In SIGIR\u201911\u2014Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, 2011 (pp. 475\u2013484). Association for Computing Machinery.","DOI":"10.1145\/2009916.2009981"},{"key":"9661_CR76","unstructured":"Singhal, P., & Bhattacharyya, P. (2016). Borrow a little from your rich cousin: Using embeddings and polarities of English words for multilingual sentiment classification. In Proceedings of COLING 2016, the 26th international conference on computational linguistics: Technical papers, 2016 (pp. 3053\u20133062)."},{"key":"9661_CR77","doi-asserted-by":"crossref","unstructured":"Souza, B. A., Almeida, T. G., Menezes, A. A., Nakamura, F. G., Figueiredo, C. M., & Nakamura, E. F. (2016). For or against? Polarity analysis in tweets about impeachment process of Brazil President. In Proceedings of the 22nd Brazilian symposium on multimedia and the web, 2016 (pp. 335\u2013338). ACM.","DOI":"10.1145\/2976796.2988216"},{"key":"9661_CR78","doi-asserted-by":"crossref","unstructured":"Souza, E., Alves, T., Teles, I., Oliveira, A. L., & Gusm\u00e3o, C. (2016). TOPIE: An open-source opinion mining pipeline to analyze consumers\u2019 sentiment in Brazilian Portuguese. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 9727, pp. 95\u2013105). Springer.","DOI":"10.1007\/978-3-319-41552-9_9"},{"key":"9661_CR79","doi-asserted-by":"crossref","unstructured":"Souza, F., Nogueira, R., & Lotufo, R. (2020). BERTimbau: Pretrained BERT models for Brazilian Portuguese. In Brazilian conference on intelligent systems, 2020 (pp. 403\u2013417). Springer.","DOI":"10.1007\/978-3-030-61377-8_28"},{"key":"9661_CR81","unstructured":"Souza, M., & Vieira, R. (2011). Construction of a Portuguese opinion lexicon from multiple resources. In Anais do Simp\u00f3sio Brasileiro de Tecnologia da Informa\u00e7\u00e3o e da Linguagem Humana, 2011, Brasil."},{"key":"9661_CR82","doi-asserted-by":"crossref","unstructured":"Souza, M., & Vieira, R. (2012). Sentiment analysis on Twitter data for Portuguese language. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), LNAI (Vol. 7243, pp. 241\u2013247). Springer.","DOI":"10.1007\/978-3-642-28885-2_28"},{"key":"9661_CR83","doi-asserted-by":"publisher","unstructured":"Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In A. Korhonen, D. R. Traum & L. M\u00e0rquez (Eds.), Proceedings of the 57th conference of the Association for Computational Linguistics, ACL 2019: Long papers, Florence, Italy, July 28\u2013August 2, 2019 (Vol. 1, pp. 3645\u20133650). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1355.","DOI":"10.18653\/v1\/p19-1355"},{"key":"9661_CR84","doi-asserted-by":"crossref","unstructured":"Strubell, E., Ganesh, A., & McCallum, A. (2020). Energy and policy considerations for modern deep learning research. In The thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, the thirty-second innovative applications of artificial intelligence conference, IAAI 2020, the tenth AAAI symposium on educational advances in artificial intelligence, EAAI 2020, New York, NY, USA, February 7\u201312, 2020 (pp. 13693\u201313696). AAAI Press. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/7123","DOI":"10.1609\/aaai.v34i09.7123"},{"issue":"2","key":"9661_CR85","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1162\/COLI_a_00049","volume":"37","author":"M Taboada","year":"2011","unstructured":"Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267\u2013307.","journal-title":"Computational Linguistics"},{"key":"9661_CR86","doi-asserted-by":"crossref","unstructured":"Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014). Learning sentiment-specific word embedding for Twitter sentiment classification. In Proceedings of the 52nd annual meeting of the Association for Computational Linguistics: Long papers, 2014 (Vol. 1, pp. 1555\u20131565).","DOI":"10.3115\/v1\/P14-1146"},{"key":"9661_CR87","doi-asserted-by":"crossref","unstructured":"Vargas, F. A., Santos, R. S. S. D., & Rocha, P. R. (2020). Identifying fine-grained opinion and classifying polarity on coronavirus pandemic. In Proceedings of the 9th Brazilian conference on intelligent systems, LNAI, 2020 (Vol. 12319, pp. 511\u2013520). Springer.","DOI":"10.1007\/978-3-030-61377-8_35"},{"key":"9661_CR89","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need, 5998\u20136008."},{"key":"9661_CR90","doi-asserted-by":"crossref","unstructured":"Vilhagra, L. A., Fernandes, E. R., & Nogueira, B. M. (2020). TextCSN: A semi-supervised approach for text clustering using pairwise constraints and convolutional Siamese network. In Proceedings of the ACM symposium on applied computing, 2020 (pp. 1135\u20131142). Association for Computing Machinery.","DOI":"10.1145\/3341105.3374018"},{"key":"9661_CR91","unstructured":"Virtanen, A., Kanerva, J., Ilo, R., Luoma, J., Luotolahti, J., Salakoski, T., Ginter, F., & Pyysalo, S. (2019). Multilingual is not enough: BERT for Finnish. CoRR abs\/1912.07076 . arxiv:1912.07076"},{"key":"9661_CR93","doi-asserted-by":"crossref","unstructured":"Vit\u00f3rio, D., Souza, E., & Oliveira, A. L. (2019). Evaluating active learning sampling strategies for opinion mining in Brazilian politics corpora. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), LNAI (Vol. 11805, pp. 695\u2013707). Springer.","DOI":"10.1007\/978-3-030-30244-3_57"},{"key":"9661_CR92","unstructured":"Vit\u00f3rio, D., Souza, E. P. R., Pereira, I., & Oliveira, A. (2017). Investigating opinion mining through language varieties: A case study of Brazilian and European Portuguese tweets. In Proceedings of the 11th Brazilian symposium in information and human language technology, 2017 (pp. 43\u201352). Sociedade Brasileira de Computa\u00e7\u00e3o, Uberl\u00e2ndia."},{"key":"9661_CR94","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., & Rush, A. M. (2020). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: System demonstrations, 2020 (pp. 38\u201345). Association for Computational Linguistics (Online). https:\/\/www.aclweb.org\/anthology\/2020.emnlp-demos.6","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"9661_CR95","doi-asserted-by":"crossref","unstructured":"Yagui, M., & Maia, L. (2017). Data mining of social manifestations in Twitter: An ETL approach focused on sentiment analysis. In XIII Brazilian symposium on information systems, 2017 (pp. 1\u20138). Sociedade Brasileira de Computacao.","DOI":"10.5753\/sbsi.2017.6019"},{"key":"9661_CR96","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cer, D., Ahmad, A., Guo, M., Law, J., Constant, N., Abrego, G. H., Yuan, S., Tar, C., Sung, Y. H., Strope & B., Kurzweil, R. (2020). Multilingual universal sentence encoder for semantic retrieval. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 87\u201394, Online. Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.acl-demos.12"}],"container-title":["Language Resources and Evaluation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10579-023-09661-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10579-023-09661-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10579-023-09661-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T15:05:42Z","timestamp":1710687942000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10579-023-09661-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,28]]},"references-count":96,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9661"],"URL":"https:\/\/doi.org\/10.1007\/s10579-023-09661-4","relation":{},"ISSN":["1574-020X","1574-0218"],"issn-type":[{"type":"print","value":"1574-020X"},{"type":"electronic","value":"1574-0218"}],"subject":[],"published":{"date-parts":[[2023,6,28]]},"assertion":[{"value":"27 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}