Análise de Sentimento de Postagens de Usuários no Twitter Combinando GPT-3 e Aprendizado de Máquina: Um Estudo de Caso Sobre o 2º Turno das Eleições Presidências Brasileiras
Resumo
Nos últimos anos, a influência das mídias sociais nas eleições em todo o mundo tornou-se cada vez mais evidente, como exemplo, o Twitter. Os textos postados no Twitter têm atraído atenção significativa como uma importante fonte de informações que podem orientar muitos processos de tomada de decisão. No entanto, torna-se difícil analisar manualmente todos os comentários sobre um determinado assunto na internet. Neste contexto, o objetivo deste estudo é explorar a aplicação da combinação do GPT-3 e Aprendizado de Máquina para a análise de sentimento de postagens de usuários no Twitter durante o segundo turno das eleições presidenciais brasileiras de 2022. Os resultados revelam que a combinação do GPT-3 e Aprendizado de Máquina foi capaz de classificar e identificar com precisão os sentimentos das postagens de usuários no Twitter. O método proposto, obteve uma acurácia de 90,88% usando o algoritmo de classificação Multinomial Naive Bayes.Referências
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Biradar, S. H., Gorabal, J., and Gupta, G. (2022). Machine learning tool for exploring sentiment analysis on twitter data. Materials Today: Proceedings, 56:1927–1934.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
Floridi, L. and Chiriatti, M. (2020). Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines, 30:681–694.
Jain, T., Mathur, S., Ninnad, A., Nikshep, B., and Chalil, N. (2022). Analyzing of political tweets in hindi language using machine learning and deep learning. In 2022 IEEE International Conference on Data Science and Information System (ICDSIS), pages 1–5. IEEE.
Kheiri, K. and Karimi, H. (2023). Sentimentgpt: Exploiting gpt for advanced sentiment analysis and its departure from current machine learning. arXiv preprint arXiv:2307.10234.
Lammerse, M., Hassan, S. Z., Sabet, S. S., Riegler, M. A., and Halvorsen, P. (2022). Human vs. gpt-3: The challenges of extracting emotions from child responses. In 2022 14th International Conference on Quality of Multimedia Experience (QoMEX), pages 1–4. IEEE.
Lee, J., Warner, E., Shaikhouni, S., Bitzer, M., Kretzler, M., Gipson, D., Pennathur, S., Bellovich, K., Bhat, Z., Gadegbeku, C., et al. (2022). Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease. Scientific Reports, 12(1):4832.
Mathew, L. and Bindu, V. (2020). A review of natural language processing techniques for sentiment analysis using pre-trained models. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pages 340–345. IEEE.
Mishra, P., Patil, S. A., Shehroj, U., Aniyeri, P., and Khan, T. A. (2022). Twitter sentiment analysis using naive bayes algorithm. In 2022 3rd International Informatics and Software Engineering Conference (IISEC), pages 1–5. IEEE.
Pandya, V., Somthankar, A., Shrivastava, S. S., and Patil, M. (2021). Twitter sentiment analysis using machine learning and deep learning techniques. In 2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4), pages 1–5. IEEE.
Pawar, C. S. and Makwana, A. (2022). Comparison of bert-base and gpt-3 for marathi text classification. In Futuristic Trends in Networks and Computing Technologies: Select Proceedings of Fourth International Conference on FTNCT 2021, pages 563–574. Springer.
Pradana, A. W. and Hayaty, M. (2019). The effect of stemming and removal of stopwords on the accuracy of sentiment analysis on indonesian-language texts. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pages 375–380.
Rodríguez-Ibánez, M., Casánez-Ventura, A., Castejón-Mateos, F., and Cuenca-Jiménez, P.-M. (2023). A review on sentiment analysis from social media platforms. Expert Systems with Applications, page 119862.
Singh, S., Kumar, K., and Kumar, B. (2022). Sentiment analysis of twitter data using tf-idf and machine learning techniques. In 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), volume 1, pages 252–255. IEEE.
Sohrabi, M. K. and Hemmatian, F. (2019). An efficient preprocessing method for supervised sentiment analysis by converting sentences to numerical vectors: a twitter case study. Multimedia tools and applications, 78(17):24863–24882.
Wang, C.-C., Day, M.-Y., and Wu, C.-L. (2022). Political hate speech detection and lexicon building: A study in taiwan. IEEE Access, 10:44337–44346.
Publicado
16/10/2023
Como Citar
PEREIRA, Ronilson; ALVES, André; VIDAL, Douglas; MOURA, Flávio; CABRAL, Laura; PAULINO, Rita; SERRUFO, Marcos; FIGUEIREDO, Karla.
Análise de Sentimento de Postagens de Usuários no Twitter Combinando GPT-3 e Aprendizado de Máquina: Um Estudo de Caso Sobre o 2º Turno das Eleições Presidências Brasileiras. In: WORKSHOP SOBRE ASPECTOS DA INTERAÇÃO HUMANO-COMPUTADOR NA WEB SOCIAL (WAIHCWS), 14. , 2023, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 20-27.
ISSN 2596-0296.
DOI: https://doi.org/10.5753/waihcws.2023.233507.
