The goal of this project is to build a simple web application to predict the sentiment of movie reviews in real-time. The web page will send new reviews off to a deployed Recurrent Neural Network model using Amazon SageMaker to predict the sentiment of entered reviews.
The project requires Python 3.6 and the following Python libraries installed
The latest version of PyTorch library also needs to be installed to implement the Neural Network model.
The dataset used for the task of binary sentiment classification is the IMDb dataset. The corpus consists of 25,000 highly polar movie reviews for training and 25,000 for testing.
The notebook and Python files are provided in the project directory to get started with the project.
To open the .ipynb files in your browser and look at the output of the completed cells, use the following command in your terminal after changing the working directory to the project directory deploy-sentiment-analysis-model:
jupyter notebook <file_name>.ipynb
To run and execute all the cells from scratch, you need to create a Jupyter notebook instance in the Amazon Sagemaker service, configure the lifecycle of the notebook, and attache the Github repository to the instance. Once set up, you can run the cells to upload the data and save any model artifacts of the trained model in AWS S3 service
To deploy the model and make the web app interact with the deployed model, you need to implement SageMaker's additional functionalities like Amazon Lambda and API Gateway. The structure for the web app is provided in the diagram Web App Diagram.svg.