yiyanghkust

--- language: "en" tags: - financial-sentiment-analysis - sentiment-analysis widget: - text: "growth is strong and we have plenty of liquidity" ---

ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. FinBERT-esg-9-categories is a FinBERT model fine-tuned on about 14,000 manually annotated sentences from firms' ESG reports and annual reports. finbert-esg-9-categories classifies a text into nine fine-grained ESG topics: Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, and Non-ESG. This model complements finbert-esg which classifies a text into four coarse-grained ESG themes (E, S, G or None). Detailed description of the nine fine-grained ESG topic definition, some examples for each topic, training sample, and the model’s performance can be found here. Output: Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, or Non-ESG. How to use You can use this model with Transformers pipeline for fine-grained ESG 9 categories classification. If you use the model in your academic work, please cite the following paper: Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." Contemporary Accounting Research (2022).

Financial Sentiment Analysis in Chinese This is a fine-tuned version of FinBERT, based on bert-base-chinese, on a private dataset (around ~8k analyst report sentences) for sentiment analysis. Test Accuracy = 0.88 Test Macro F1 = 0.87 Labels: 0 -> Neutral; 1 -> Positive; 2 -> Negative