Financial market participants are faced with an overload of information that influences their decisions, and sentiment analysis stands out as a useful tool to help separate out the relevant and meaningful facts and figures. However, the same piece of news can have a positive or negative impact on stock prices, which presents a challenge for this task. Sentiment analysis and other natural language programming (NLP) tasks often start out with pre-trained NLP models and implement fine-tuning of the hyperparameters to adjust the model to changes in the environment. Transformer-based language models such as BERT (Bidirectional Transformers for Language Understanding) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. To account for changes in the economic environment, the model needs to be fine-tuned once more when the data starts drifting

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