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Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting
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corresponding code to the paper:https://arxiv.org/abs/2502.14897
- Neptune AI results for language model experiments can be foundhere including tables, confusion matrixes and more.
- the main notebook on Kaggle called tweet-classification can be foundhere
- the final implementation and optimization of Triple Barrier Labeling can be found in the notebooknext_day_prediction

- backtesting experiments and results can be foundhere
use poetry to install the packages withpoetry install. for more information go to poetrydocs
then run withpython src/run.py [Experiment ID]
Here are the folders and what they contain:
- raw: unprocessed data
- dataset: processed data
- notebook: notebooks
- src: contains the source code for experiments
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Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting
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