Sentiment Analysis in Green Finance with LLMs

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Green finance has gained global significance as governments and financial institutions emphasize sustainable investment. Understanding the sentiment of green finance reports can provide valuable insights into public perception, investor sentiment, and policy reception. This study uses three different models FinBERT, GPT-3.5 Turbo, and GPT-4o -- to perform sentiment analysis on over 1000 reports obtained from the International Finance Corporation (IFC) website. To assess the accuracy of the models, this paper manually labeled the sentiment of the reports into three categories: Positive, Negative, and Neutral. We compared the models outputs using standard metrics such as F1-score, Accuracy, Precision, and Recall. The findings indicate that GPT-3.5 Turbo outperforms the other models in terms of accuracy. GPT-4o shows superior performance compared to Finbert which trained on financial texts in extracting sentiment from general text. Even though FinBERT and GPT-4 have stronger financial text processing capabilities, GPT-3.5 Turbo can often capture the true intent and sentiment of the text more quickly and clearly, especially when trained on a relatively small text corpus. Its generalization and speed make it efficient for less complex financial tasks.