Summary of Financial Sentiment Analysis: Leveraging Actual and Synthetic Data For Supervised Fine-tuning, by Abraham Atsiwo
Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning
by Abraham Atsiwo
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper proposes a novel approach to sentiment analysis in finance, leveraging general-purpose language models fine-tuned on curated labeled data. The authors introduce two models, BertNSP-finance and finbert-lc, which concatenate shorter financial sentences into longer ones and determine sentiment from digital text, respectively. These models are evaluated on the financial phrasebank data, demonstrating improved performance on accuracy and F1 score at both 50% and 100% agreement levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to understand how people feel when they read news about money. It’s trying to figure out if the news makes investors happy or sad. The problem is that most computer programs are too general to understand financial news well. To solve this, scientists created special models that can analyze financial text better. They tested these models on a dataset and found that they worked really well. |
Keywords
* Artificial intelligence * F1 score
