Domain-Specific NLP Models for Financial Sentiment Analysis

NLP language models form the backbone of social media listening tools and are responsible for the correct identification and classification of online sentiment. With the popularity of these models surging, there has been an increase in the development of domain-specific NLP models that are designed to analyze financial data. These models are trained on vast amounts of financial data and news, enabling them to provide more accurate insights and analysis than generic NLP models.

Bloomberg-GPT Finance-Specific NLP Model

Late last month, financial intelligence giant Bloomberg made headlines with its new Bloomberg-GPT NLP model. The model is an adaptation of the massively popular GPT and has been specifically designed for analyzing financial sentiment. The model has been trained on vast amounts of financial news and data points, leveraging Bloomberg’s formidable R&D capacity.

The model’s release article by Bloomberg specifies that Bloomberg-GPT outperformed a number of other NLP models. However, the models that were pitted against Bloomberg-GPT in these tests, GPT-NeoX, OPT-66B, and BLOOM-176B, are all general-purpose models. Curiously, the Bloomberg researchers haven’t run, or at least haven’t reported on, any comparative tests using Bloomberg-GPT and other finance-specific language models.

Bloomberg-GPT is the first large NLP model in the finance domain that has major corporate backing. The headlines generated by the model’s introduction might lead some to believe that this is the first finance-specific model at all. However, at least one powerful NLP model for the financial domain has been in existence since 2019.

FinBERT - The Pioneer of Finance NLP Models

FinBERT was one of the first open-source NLP models specifically designed for the finance industry. It was adapted from the popular general-purpose BERT model and has gone through numerous adaptations and improvements since its inception in 2019. FinBERT is among the most powerful, if not the most powerful, financial sentiment models available. It is one of the NLP models used within our social media sentiment platform, PUMP.

One of the biggest advantages of FinBERT is its ability to analyze financial text with high accuracy. It is capable of understanding financial jargon and can identify the sentiment of text related to specific companies or market trends.

Being an open-source model, FinBERT has been adapted on numerous occasions, with other NLP models based on it available on the market. Over the coming weeks and months, it will be interesting to see the results of in-depth tests comparing FinBERT and Bloomberg-GPT. This would be the ultimate Battle Royale of the year for finance NLP models.

ESG-BERT – A Model for Sustainable Investors

While FinBERT is the most popular finance-specific model, there are derivatives of it and of its parent BERT model that are applicable to more specialised finance niches. One interesting niche model among these is ESG-BERT.

ESG-BERT is a model that has been specifically designed for sustainable investors. The model is derived from the BERT model and has been trained on financial data related to environmental, social, and governance (ESG) investments. ESG-BERT is much smaller in popularity than FinBERT, but it is probably the leading NLP model for analyzing financial text with a focus on sustainable investments.

Domain-specific NLP models are becoming increasingly important in the finance industry. Bloomberg-GPT, FinBERT, and ESG-BERT are three examples of such models. The first one has the backing of a corporate giant. Expect Bloomberg to promote and improve this model furiously over the coming months. The second one, FinBERT, stands out in this niche thanks to its power and popularity. ESG-BERT, on the other hand, is a less-known model that caters to the needs of those interested in sustainable investment. The field of finance-related NLP models is expanding. Expect 2023 to be the year of proliferation of such models.