Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines

TL;DR

This paper fine-tunes the Gemma-7B language model for sentiment analysis of financial news headlines, demonstrating its superior performance in accurately classifying investor sentiment and aiding financial decision-making.

Contribution

It introduces a fine-tuning approach for Gemma-7B on financial sentiment analysis, showing its effectiveness over other models like DistilBERT and Llama.

Findings

Gemma-7B achieved the highest precision, recall, and F1 scores after fine-tuning.

Fine-tuned Gemma-7B significantly improved accuracy in financial sentiment classification.

The model offers valuable insights for market analysis and investment decisions.

Abstract

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.