Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines
This provides a tool for financial stakeholders like investors to analyze sentiment for market insights and risk management, but it is incremental as it applies fine-tuning to existing models on a specific dataset.
The study tackled sentiment analysis of financial news headlines by fine-tuning models like gemma-7B, which outperformed others with the highest precision, recall, and F1 scores, showing significant accuracy improvements.
In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural Language Processing (NLP) and Large Language Models (LLM), we analyze sentiment from the perspective of retail investors. The FinancialPhraseBank dataset, which contains categorized sentiments of financial news headlines, serves as the basis for our analysis. We fine-tuned several models, including distilbert-base-uncased, Llama, and gemma-7b, to evaluate their effectiveness in sentiment classification. Our experiments demonstrate that the fine-tuned gemma-7b model outperforms others, achieving the highest precision, recall, and F1 score. Specifically, the gemma-7b model showed significant improvements in accuracy after fine-tuning, indicating its robustness in capturing the nuances of financial sentiment. This model can be instrumental in providing market insights, risk management, and aiding investment decisions by accurately predicting the sentiment of financial news. The results highlight the potential of advanced LLMs in transforming how we analyze and interpret financial information, offering a powerful tool for stakeholders in the financial industry.