In this study, we propose a novel method for constructing datasets for financial sentiment analysis．In recentyears, there has been growing interest in research aimed at supporting decision-making for investors and analysts．Among such approaches, sentiment analysis―which transforms text data into quantitative values to enable statisticalanalysis―has gained particular attention．Existing studies have primarily employed methods such as coarse multilabelclassification of text polarity or summing up scores assigned to individual words．However, these approachesoften fail to consider contextual information or to capture the subtle nuances specific to financial texts. The methodproposed in this study addresses these issues by leveraging two techniques: text comparison and a rating system．By interpreting the results of sentiment comparisons between texts as pseudo-matches, we can apply existing ratingsystems．The resulting sentiment scores are continuous values, which is expected to better reflect the strength ofpolarity in financial texts. Experimental results show that the sentiment data generated using the proposed methodexhibits trends similar to those of human-annotated data．Furthermore, training BERT on the generated training datademonstrated that the data quality is high enough to serve as effective supervision.
Nakaya et al. (Wed,) studied this question.