PT  - JOURNAL ARTICLE
AU  - Zhengzheng Yang
AU  - Le Zhang
AU  - Xiaoyun Wang
AU  - Yubo Mai
TI  - ESG Text Classification: An Application of the Prompt-Based Learning Approach
AID  - 10.3905/jfds.2022.1.115
DP  - 2023 Jan 31
TA  - The Journal of Financial Data Science
PG  - 47--57
VI  - 5
IP  - 1
4099  - https://pm-research.com/content/5/1/47.short
4100  - https://pm-research.com/content/5/1/47.full
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.