This project performs sentiment analysis on IMDb movie reviews using a Simple Recurrent Neural Network (RNN). The goal is to classify movie reviews as positive or negative based on the text content, achieving an impressive 90% accuracy.
- Built a sentiment analysis model using an RNN architecture.
- Classified IMDb movie reviews into positive or negative sentiments.
- Achieved a high accuracy of 90% in sentiment prediction.
IMDb movie reviews dataset.
- Programming Languages: Python
- Libraries: NumPy, Pandas, TensorFlow, Keras, Matplotlib, Seaborn
- Development Tools: Jupyter Notebook, VS Code, Git
- Comprehensive text preprocessing, including tokenization and padding.
- Development and training of a Simple RNN for sentiment classification.
- Evaluation and validation to ensure high model performance.
- Clone this repository:
git clone https://github.com/YourUsername/IMDb-Sentiment-Analysis.git - Install required dependencies:
pip install -r requirements.txt - Run the notebook or script to train and evaluate the model.
The model achieved a 90% accuracy in classifying movie reviews as positive or negative, demonstrating its effectiveness in sentiment analysis tasks.
Contributions are welcome! Feel free to fork this repository, create a branch, and submit a pull request.