https://github.com/sayamalt/financial-news-sentiment-analysis
Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.
https://github.com/sayamalt/financial-news-sentiment-analysis
data-exploration-and-preprocessing distilbert-model fine-tune-bert-tensorflow hugging-face-transformers model-architecture-and-implementation model-inference model-training-and-evaluation multiclass-classification natural-language-processing sentiment-analysis text-preprocessing text-tokenization
Last synced: 3 months ago
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Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.
- Host: GitHub
- URL: https://github.com/sayamalt/financial-news-sentiment-analysis
- Owner: SayamAlt
- License: apache-2.0
- Created: 2024-05-06T05:58:32.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-06T06:41:40.000Z (about 1 year ago)
- Last Synced: 2024-12-28T08:09:34.562Z (5 months ago)
- Topics: data-exploration-and-preprocessing, distilbert-model, fine-tune-bert-tensorflow, hugging-face-transformers, model-architecture-and-implementation, model-inference, model-training-and-evaluation, multiclass-classification, natural-language-processing, sentiment-analysis, text-preprocessing, text-tokenization
- Language: Jupyter Notebook
- Homepage:
- Size: 745 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# About Dataset
## Context
This dataset (FinancialPhraseBank) contains the sentiments for financial news headlines from the perspective of a retail investor.
## Content
The dataset contains two columns, "Sentiment" and "News Headline". The sentiment can be negative, neutral or positive.
## Acknowledgements
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014). Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology, 65(4), 782-796.