Transfer Learning and Transformer Architecture for Financial Sentiment Analysis
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914782739693568 |
|---|---|
| author | Rehman, Tohida Bose, Raghubir Chattopadhyay, Samiran Sanyal, Debarshi Kumar |
| author_facet | Rehman, Tohida Bose, Raghubir Chattopadhyay, Samiran Sanyal, Debarshi Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01586 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transfer Learning and Transformer Architecture for Financial Sentiment Analysis Rehman, Tohida Bose, Raghubir Chattopadhyay, Samiran Sanyal, Debarshi Kumar Computation and Language 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. |
| title | Transfer Learning and Transformer Architecture for Financial Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.01586 |