RT Journal Article
SR Electronic
T1 ESG Text Classification: An Application of the Prompt-Based Learning Approach
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP 47
OP 57
DO 10.3905/jfds.2022.1.115
VO 5
IS 1
A1 Zhengzheng Yang
A1 Le Zhang
A1 Xiaoyun Wang
A1 Yubo Mai
YR 2023
UL https://pm-research.com/content/5/1/47.abstract
AB Over the past decade, there is a surging trend to integrate environmental, social, and governance (ESG) criteria into financial decision making. ESG information extracted manually from text sources, such as company statements, press releases, and regulatory disclosures, can be expensive and inconsistent due to human interpretation. In this article, the authors introduce the application of prompt-based learning, a cutting-edge natural language processing (NLP) technology, to classify textual data into ESG and non-ESG categories. In particular, the authors establish a prompt-based ESG classifier, using data from Refinitiv, and benchmark it against a traditional pre-train and fine-tune classifier through statistical test. The authors fine-tune the classifiers on various sizes of training data. The experiment shows that the prompt-based learning approach outperforms the traditional pre-train and fine-tune classifier and can generate promising results when training data are limited.