Towards machine learning based text categorization in the financial domain

Despite the widespread research on text categorization in various Natural Language Processing (NLP) domains, there exists a noticeable void concerning its application to financial data. This study addresses this gap by employing pre-trained Bidirectional Encoder Representations from Trans-formers (BERT) models, fine-tuned specifically for the financial domain, to categorize newspaper articles focusing on financial topics. This is the first time that the dataset presented in this paper has been used. Further we evaluate the efficacy of established models in sentiment prediction using these rather long texts. Finally, we delve into the intricacies of company-specific sentiment and relevance prediction within these articles, acknowl-edging the prevalence of multiple companies being mentioned in one article, thus contributing to a more nuanced understanding of text analysis in the financial sector.