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This research paper investigates the application of sentiment analysis in the fintech sector, focusing on stock market prediction through a transformer-based model, specifically FinBERT. By comparing its performance against established models like CNN, LSTM, and BERT across different datasets, the study demonstrates that FinBERT achieves superior accuracy in classifying sentiments from financial reviews. The findings emphasize the significance of specialized models tailored to specific domains for improving sentiment analysis within the financial sector, providing useful information for those involved in the fintech field.

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Fintech Sentiment Analysis Using Deep Learning Models

  • 会议论文

  • 发表日期：

    2025年

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  • Sarah Anis,
  • Mohamed Mabrouk,
  • Mostafa Aref

摘要

This research paper investigates the application of sentiment analysis in the fintech sector, focusing on stock market prediction through a transformer-based model, specifically FinBERT. By comparing its performance against established models like CNN, LSTM, and BERT across different datasets, the study demonstrates that FinBERT achieves superior accuracy in classifying sentiments from financial reviews. The findings emphasize the significance of specialized models tailored to specific domains for improving sentiment analysis within the financial sector, providing useful information for those involved in the fintech field.