Transfer Learning and Transformer Architecture for Financial Sentiment Analysis
<|reference_start|>Transfer Learning and Transformer Architecture for Financial Sentiment Analysis: Financial sentiment analysis allows financial institutions like Banks and Insurance Companies to better manage the credit scoring of their customers in a better way. Financial domain uses specialized mechanisms which makes sentiment analysis difficult. In this paper, we propose a pre-trained language model which can help to solve this problem with fewer labelled data. We extend on the principles of Transfer learning and Transformation architecture principles and also take into consideration recent outbreak of pandemics like COVID. We apply the sentiment analysis to two different sets of data. We also take smaller training set and fine tune the same as part of the model.<|reference_end|>
@article{rehman2024transfer,
    title={Transfer Learning and Transformer Architecture for Financial Sentiment
  Analysis},
    author={Tohida Rehman, Raghubir Bose, Samiran Chattopadhyay, Debarshi Kumar
  Sanyal},
    journal={Proceedings of International Conference on Computational
  Intelligence, Data Science and Cloud Computing: IEM-ICDC 2021,pages 17--27},
    year={2024},
    doi={10.1007/978-981-19-1657-1_2},
    archivePrefix={arXiv},
    eprint={2405.01586},
    primaryClass={cs.CL}
}