DeepSentiment: IMDb Movie Review Sentiment Analysis

Overview

DeepSentiment is a deep learning-based sentiment analysis project designed to classify movie reviews from the IMDb dataset as positive or negative. The model leverages a Bidirectional Long Short-Term Memory (LSTM) network with a self-attention mechanism to capture both the sequential dependencies and contextual relationships within the text.

Key Features

• State-of-the-Art Architecture: Utilizes a powerful combination of Bidirectional LSTM and self-attention, known for its effectiveness in natural language processing tasks.
• High Accuracy: Achieves an impressive 89.26% accuracy on the IMDb test set, demonstrating strong performance in sentiment classification.
• Open to Contributions: Contributions from the community are welcome to further enhance and improve the model.

Getting Started

1. Clone the Repository:

   git clone [https://github.com/](https://github.com/)tafartech/DeepSentiment.git
   cd DeepSentiment

2. Install Dependencies:

   pip install -r requirements.txt

3. Download the IMDb Dataset: (If not already included)

   python download_imdb.py  

4. Train the Model:

   python train.py

5. Evaluate the Model:

   python evaluate.py

Results

The trained model achieves an accuracy of 89.26% on the IMDb test set. You can find the confusion matrix and classification report in the results Output.

Call for Contributions

Some areas where you can help:

• Hyperparameter Tuning: Explore different configurations to find optimal values for learning rate, batch size, and model architecture parameters.
• Regularization Techniques: Experiment with L2 regularization, additional dropout layers, or other methods to address the slight overfitting observed in the validation loss.
• Alternative Architectures: Try implementing different neural network architectures like Convolutional Neural Networks (CNNs) or incorporating pre-trained word embeddings like GloVe or Word2Vec.
• Ensemble Methods: Investigate the use of ensemble learning techniques to combine multiple models and potentially achieve higher accuracy.

Contributing

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature-name).
3. Make your changes and commit them (git commit -m 'Add your feature').
4. Push to the branch (git push origin feature/your-feature-name).
5. Open a pull request.

Let's collaborate to make DeepSentiment even better.