FinancialBERT for Sentiment Analysis FinancialBERT is a BERT model pre trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model. The model was fine tuned for Sentiment Analysis task on Financial PhraseBank dataset. Experiments show that this model outperforms the general BERT and other financial domain specific models. More details on FinancialBERT 's pre training process can be found at: https://www.researchgate.net/publication/358284785 FinancialBERT A Pretrained Language Model for Financial Text Mining Training data FinancialBERT model was fine tuned on Financial PhraseBank, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). Fine tuning hyper parameters learning rate = 2e 5 batch size = 32 max seq length = 512 num train epochs = 5 Evaluation metrics The evaluation metrics used are: Precision, Recall and F1 score. The following is the classification report on the test set. sentiment precisi…
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