Professionalism-Aware Pre-Finetuning for Profitability Ranking
Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi, Yusuke Miyao · 2024
Opinion mining, specifically in the investment sector, has experienced a significant increase in interest over recent years. This paper presents a novel approach to overcome current limitations in assessing and ranking investor opinions based on profitability. The study introduces a pre-finetuning scheme to improve language models' capacity to distinguish professionalism, thus enabling ranking of all available opinions. Furthermore, the paper evaluates ranking results using traditional metrics and suggests the use of a pairwise setting for better performances over a regression setting. Lastly, our method is shown to be effective across various investor opinion tasks, encompassing both professional and amateur investors. The results indicate that this approach significantly enhances the efficiency and accuracy of opinion mining in the investment sector.