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Sentiment Analysis models have numerous applications, including evaluating business performance through comments and reviews. This capability helps businesses understand the public perception of their products and services and identify areas for improvement. However, a significant limitation in developing such models for the Portuguese language is the lack of labeled datasets, which restricts effective model training. This article addresses this issue by collecting 375,912 comments from Twitter/X, focusing specifically on comments about Brazilian banks due to the public's widespread use of their services. The labeled dataset currently contains 1,096 comments labeled as Positive, Neutral, or Negative. We present results in fine-tuning Sentiment Analysis models based on this dataset. We found it holds great potential for providing insights into customer perceptions and market trends within the banking sector. By leveraging this dataset, businesses can gain a valuable understanding of their market position and areas for service improvement.

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#	Name
1	Gabriel Kakimoto(g234878@dac.unicamp.br)
2	Seyed Haddadi(seyed@unicamp.br)
3	Fillipe Silva(f212148@dac.unicamp.br)
4	Patrick Araújo(p217144@dac.unicamp.br)
5	Marcelo Reis(msreis@unicamp.br)
6	Julio Dos Reis(jreis@ic.unicamp.br)

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