Expansão automática de léxico para Análise de Sentimentos de Twitter no domínio do Mercado Financeiro Brasileiro
Resumo
Este artigo investiga as oportunidades na criação de léxicos especializados com foco na construção de um glossário em Português voltado para o Mercado Financeiro Brasileiro (MFB). A metodologia empregada envolve a concepção de uma sequência de etapas visando enriquecer um conjunto de palavras semente, que é posteriormente utilizado na tarefa de análise de sentimentos em tweets e notícias relacionadas ao domínio do MFB. Como resultados, foram alcançados um f1-score de 71,5% na classificação de tweets e um f1-score de 67,9% em notícias, ambos na abordagem lexical. Além disso, uma abordagem mista, combinando o léxico com o modelo de aprendizagem de máquina support vector machine, atingiu um f1-score de 77,4% na classificação de tweets.
Referências
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Das, S. R., Donini, M., Zafar, M. B., He, J., and Kenthapadi, K. (2022). Finlex: An effective use of word embeddings for financial lexicon generation. Journal of Finance and Data Science, 8:1–11.
Fernandes, D. S. A., Fernandes, M. G. C., Borges, G. A., and Soares, F. A. (2019). Decision-making simulator for buying and selling stock market shares based on twitter indicators and technical analysis. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 2626–2632.
Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Rodrigues, J., and Aluisio, S. (2017). Portuguese word embeddings: Evaluating on word analogies and natural language tasks.
Januário, B. A., Carosia, A. E. d. O., Silva, A. E. A. d., and Coelho, G. P. (2022). Sentiment analysis applied to news from the Brazilian stock market. IEEE Latin America Transactions, 20:512–518.
Jung, E., Jain, H., Sinha, A. P., and Gaudioso, C. (2021). Building a specialized lexicon for breast cancer clinical trial subject eligibility analysis. Health Informatics Journal, 27.
Losada, D. E. and Gamallo, P. (2020). Evaluating and improving lexical resources for detecting signs of depression in text. Language Resources and Evaluation, 54:1–24.
Loughran, T. and Mcdonald, B. (2011). When is a liability not a liability? textual analysis, dictionaries, and 10-ks. Journal of Finance, 66:35–65.
Mahmood, A. T., Kamaruddin, S. S., Naser, R. K., and Nadzir, M. M. (2020). A combination of lexicon and machine learning approaches for sentiment analysis on facebook. Journal of System and Management Sciences, 10:140–150.
Oliveira, N., Cortez, P., and Areal, N. (2016). Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decision Support Systems, 85:62–73.
Pereira, D. A. (2021). A survey of sentiment analysis in the Portuguese language. Artificial Intelligence Review, 54:1087–1115.
Shan, R., Jiang, T., and Wang, Y. (2021). Research on the construction of domain sentiment lexicon based on label propagation algorithm. ACM International Conference Proceeding Series, pages 1024–1029.
Smywiński-Pohl, A., Lasocki, K., Wróbel, K., and Strzała, M. (2019). Automatic construction of a Polish legal dictionary with mappings to extra-legal terms established via word embeddings. Proceedings of the 17th International Conference on Artificial Intelligence and Law, ICAIL 2019, pages 234–238.
Wang, Y., Yin, F., Liu, J., and Tosato, M. (2020). Automatic construction of domain sentiment lexicon for semantic disambiguation. Multimedia Tools and Applications, 79:22355–22373.
Bos, T. and Frasincar, F. (2022). Automatically building financial sentiment lexicons while accounting for negation. Cognitive Computation, 14:442–460.
Carosia, A. E., Coelho, G. P., and Silva, A. E. (2020). Analyzing the Brazilian financial market through Portuguese sentiment analysis in social media. Applied Artificial Intelligence, 34:1–19.
Das, S. R., Donini, M., Zafar, M. B., He, J., and Kenthapadi, K. (2022). Finlex: An effective use of word embeddings for financial lexicon generation. Journal of Finance and Data Science, 8:1–11.
Fernandes, D. S. A., Fernandes, M. G. C., Borges, G. A., and Soares, F. A. (2019). Decision-making simulator for buying and selling stock market shares based on twitter indicators and technical analysis. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 2626–2632.
Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Rodrigues, J., and Aluisio, S. (2017). Portuguese word embeddings: Evaluating on word analogies and natural language tasks.
Januário, B. A., Carosia, A. E. d. O., Silva, A. E. A. d., and Coelho, G. P. (2022). Sentiment analysis applied to news from the Brazilian stock market. IEEE Latin America Transactions, 20:512–518.
Jung, E., Jain, H., Sinha, A. P., and Gaudioso, C. (2021). Building a specialized lexicon for breast cancer clinical trial subject eligibility analysis. Health Informatics Journal, 27.
Losada, D. E. and Gamallo, P. (2020). Evaluating and improving lexical resources for detecting signs of depression in text. Language Resources and Evaluation, 54:1–24.
Loughran, T. and Mcdonald, B. (2011). When is a liability not a liability? textual analysis, dictionaries, and 10-ks. Journal of Finance, 66:35–65.
Mahmood, A. T., Kamaruddin, S. S., Naser, R. K., and Nadzir, M. M. (2020). A combination of lexicon and machine learning approaches for sentiment analysis on facebook. Journal of System and Management Sciences, 10:140–150.
Oliveira, N., Cortez, P., and Areal, N. (2016). Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decision Support Systems, 85:62–73.
Pereira, D. A. (2021). A survey of sentiment analysis in the Portuguese language. Artificial Intelligence Review, 54:1087–1115.
Shan, R., Jiang, T., and Wang, Y. (2021). Research on the construction of domain sentiment lexicon based on label propagation algorithm. ACM International Conference Proceeding Series, pages 1024–1029.
Smywiński-Pohl, A., Lasocki, K., Wróbel, K., and Strzała, M. (2019). Automatic construction of a Polish legal dictionary with mappings to extra-legal terms established via word embeddings. Proceedings of the 17th International Conference on Artificial Intelligence and Law, ICAIL 2019, pages 234–238.
Wang, Y., Yin, F., Liu, J., and Tosato, M. (2020). Automatic construction of domain sentiment lexicon for semantic disambiguation. Multimedia Tools and Applications, 79:22355–22373.
Publicado
07/12/2023
Como Citar
DE SOUSA, Thiago Monteles; FERNANDES, Deborah S. A..
Expansão automática de léxico para Análise de Sentimentos de Twitter no domínio do Mercado Financeiro Brasileiro. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 11. , 2023, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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DOI: https://doi.org/10.5753/erigo.2023.237321.
