Fake News Classification using Bidirectional LSTM
The Fake News Classification project aims to tackle misinformation using advanced natural language processing techniques. By utilizing a Bidirectional Long Short-Term Memory (BiLSTM) network, this project classifies news articles as either real or fake. The BiLSTM model effectively captures the context of words from both directions in a sentence, making it a powerful tool for detecting fake news. This repository includes all necessary scripts, models, and datasets for data preprocessing, model training, and prediction.
The dataset used in this project is available in the data directory. It is in CSV (Comma-Separated Values) format.
Dataset Source Link: kaggle dataset
- Clone the repository
git clone https://github.com/priyanshudutta04/Fake-News-Classification.git
- Install dependencies
pip install -r requirements.txt
- Run the Model
jupyter notebook Fake-News-Classification.ipynb
Note: If GPU is available install cuda toolkit and cuDNN for faster execution
Contributions are welcome! If you have ideas for improving the model or adding new features, please feel free to fork the repository and submit a pull request.
This project was created for educational and research purposes only and should not be used to classify real-world news or for fact-checking. The accuracy and reliability of the model depend on the quality and scope of the training data. Users should exercise caution when interpreting the results, and double check important information. The creator disclaim any responsibility for consequences resulting from the use of this software beyond its intended educational scope.
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