Skip to content
/ Financial-News-Sentiment-Analysis Public

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%.

License

Apache-2.0 license
0 stars 0 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

SayamAlt/Financial-News-Sentiment-Analysis

BranchesTags

Repository files navigation

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.

About

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%.

Topics

natural-language-processing sentiment-analysis multiclass-classification text-preprocessing text-tokenization distilbert-model hugging-face-transformers fine-tune-bert-tensorflow model-inference model-architecture-and-implementation model-training-and-evaluation data-exploration-and-preprocessing

Resources

Readme

License

Apache-2.0 license
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages