Sentiment Analysis using XGBoost with SageMaker This repository contains a mini-project on sentiment analysis using XGBoost, developed using TensorFlow and Python. The project assumes some familiarity with Amazon SageMaker, a fully managed machine learning service. If you are new to SageMaker, please refer to the official documentation for an overview and basic usage.
Background Sentiment analysis is a natural language processing (NLP) task that involves determining the sentiment expressed in a given text, such as a movie review. In this mini-project, we leverage the power of XGBoost, a popular gradient boosting algorithm, to perform sentiment analysis on movie reviews.
Getting Started To get started with the project, follow these steps:
Set up your Python environment and install the required dependencies. Explore the provided notebooks in the notebooks/ directory to understand the data, model training process, and evaluation metrics. Execute the notebooks in sequential order, starting with data preprocessing, followed by model training and evaluation. Once the model is trained and evaluated, you can use the provided scripts in the scripts/ directory to preprocess new data and deploy the trained model using SageMaker. Usage Detailed instructions and explanations are provided within the notebooks. Follow the step-by-step process outlined in the notebooks to understand and execute each stage of the sentiment analysis project. Feel free to modify the code and experiment with different parameters to improve the model's performance.
Contributions Contributions to this project are welcome! If you find any issues, have suggestions for improvements, or want to add new features, please open an issue or submit a pull request.