Summary of Fine-tuning Gemma-7b For Enhanced Sentiment Analysis Of Financial News Headlines, by Kangtong Mo et al.
Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines
by Kangtong Mo, Wenyan Liu, Xuanzhen Xu, Chang Yu, Yuelin Zou, Fangqing Xia
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study explores applying sentiment analysis to financial news headlines to understand investor sentiment. Researchers used Natural Language Processing (NLP) and Large Language Models (LLM) to analyze sentiment from retail investors’ perspectives. The FinancialPhraseBank dataset, containing categorized sentiments of financial news headlines, served as the basis for the analysis. Several models, including distilbert-base-uncased, Llama, and gemma-7b, were fine-tuned to evaluate their effectiveness in sentiment classification. The results show that the fine-tuned gemma-7b model outperforms others, achieving high precision, recall, and F1 score. This model can provide market insights, aid investment decisions by accurately predicting financial news sentiment, and transform how we analyze financial information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at using computers to understand what people think about the stock market from reading newspaper headlines. The researchers used special computer programs called Natural Language Processing (NLP) and Large Language Models (LLM) to figure out what ordinary investors are thinking. They used a big database of news headlines with happy or sad words, like “bullish” or “bearish”. They tested different computer models on this data and found that one model, called gemma-7b, worked really well. This means it can help predict how people will react to certain news stories, which is useful for making smart investment decisions. |
Keywords
» Artificial intelligence » Classification » F1 score » Llama » Natural language processing » Nlp » Precision » Recall
