FinBERT: Financial Sentiment Analysis with Pre-trained Language Models
Table 3. Experimental Results on FiQA Sentiment Dataset
['Model', 'MSE', '[ITALIC] R2']
FinBERT outperforms ULMFit, and consequently all of the other methods in all metrics. In order to measure the performance of the models on different sizes of labeled training datasets, we ran LSTM classifiers, ULMFit and FinBERT on 5 different configurations. 100 training examples is too low for all of the models. However, once the training size becomes 250, ULMFit and FinBERT starts to successfully differentiate between labels, with an accuracy as high as 80% for FinBERT. All of the methods consistently get better with more data, but ULMFit and FinBERT does better with 250 examples than LSTM classifiers do with the whole dataset. This shows the effectiveness of language model pre-training. Our model outperforms state-of-the-art models for both MSE and R2. Since we don’t have access to that we report the results on 10-Fold cross validation.