{"id":28478790,"url":"https://github.com/securdrgorp/bigdata-amazon-reviews","last_synced_at":"2026-04-02T02:50:17.299Z","repository":{"id":295036169,"uuid":"982542712","full_name":"SecurDrgorP/bigdata-amazon-reviews","owner":"SecurDrgorP","description":"Build a system capable of predicting sentiment (positive, neutral, negative) of comments in real time (online) and displaying the results in an offline dashboard.","archived":false,"fork":false,"pushed_at":"2025-06-01T21:28:47.000Z","size":11005,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-02T07:27:48.323Z","etag":null,"topics":["amazon-reviews","big-data","docker","docker-compose","flask","kafka","mongodb","shell-scripts","spark","spark-streaming"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SecurDrgorP.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-13T03:41:09.000Z","updated_at":"2025-06-01T21:41:28.000Z","dependencies_parsed_at":"2025-05-23T10:05:15.845Z","dependency_job_id":null,"html_url":"https://github.com/SecurDrgorP/bigdata-amazon-reviews","commit_stats":null,"previous_names":["securdrgorp/bigdata-amazon-reviews"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SecurDrgorP/bigdata-amazon-reviews","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SecurDrgorP%2Fbigdata-amazon-reviews","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SecurDrgorP%2Fbigdata-amazon-reviews/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SecurDrgorP%2Fbigdata-amazon-reviews/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SecurDrgorP%2Fbigdata-amazon-reviews/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SecurDrgorP","download_url":"https://codeload.github.com/SecurDrgorP/bigdata-amazon-reviews/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SecurDrgorP%2Fbigdata-amazon-reviews/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263282530,"owners_count":23442191,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["amazon-reviews","big-data","docker","docker-compose","flask","kafka","mongodb","shell-scripts","spark","spark-streaming"],"created_at":"2025-06-07T18:00:37.624Z","updated_at":"2025-12-30T20:09:34.629Z","avatar_url":"https://github.com/SecurDrgorP.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Amazon Reviews Sentiment Analysis - Big Data Project\n\n## Overview\n\nThis project implements an end-to-end big data pipeline for analyzing Amazon product reviews. It uses a combination of data preprocessing, machine learning for sentiment analysis, and real-time streaming technologies to process reviews and visualize insights through an interactive dashboard.\n\n## Architecture\n\nThe system architecture consists of the following components:\n\n- **Data Preprocessing**: Cleans and transforms raw Amazon review data\n- **Machine Learning**: Trains and deploys a sentiment analysis model\n- **Streaming Pipeline**: Processes reviews in real-time using Kafka and Spark\n- **Storage Layer**: Stores processed data and results in MongoDB\n- **Web Dashboard**: Visualizes insights through a Flask web application\n\n## Technologies\n\n- **Apache Kafka**: Message streaming platform\n- **Apache Spark**: Distributed data processing\n- **MongoDB**: NoSQL database for storing reviews and results\n- **Flask**: Web framework for the dashboard\n- **Docker**: Containerization for easy deployment\n- **Python**: Primary programming language\n- **SpaCy**: NLP library for text processing\n\n## Installation \u0026 Setup\n\n### Prerequisites\n- Docker and Docker Compose\n- Python 3.7+\n\n### Steps\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/yourusername/bigdata-amazon-reviews.git\n   cd bigdata-amazon-reviews\n   ```\n\n2. Create and activate a virtual environment:\n   ```bash\n   # Create virtual environment\n   python -m venv venv\n   \n   # Activate virtual environment\n   # On Linux/macOS\n   source venv/bin/activate\n   # On Windows\n   venv\\Scripts\\activate\n   ```\n\n3. Create and configure environment variables:\n   ```bash\n   cp .env.example .env\n   # Edit .env with appropriate values\n   ```\n\n4. Install Python dependencies and the spaCy language model:\n   ```bash\n   pip install -r requirements.txt\n\n   python -m spacy download en_core_web_sm\n   ```\n\n5. Start Docker containers:\n   ```bash\n   docker-compose up -d\n   ```\n\n## Usage\n\n1. Run the Spark consumer to process the data:\n   ```bash\n   ./run_consumer.sh\n   ```\n\n2. Start the Kafka producer to ingest review data:\n   ```bash\n   ./run_producer.sh\n   ```\n\n3. Access the dashboard at http://localhost:5000\n\n## Data Pipeline\n\n1. **Data Preparation**: Raw Amazon review data is cleaned and preprocessed\n2. **Producer**: Kafka producer streams review data into the pipeline\n3. **Consumer**: Spark processes the streams and performs sentiment analysis\n4. **Storage**: Results are stored in MongoDB\n5. **Visualization**: Flask application renders insights through a web dashboard\n\n## Model Training\n\nThe sentiment analysis model can be retrained using:\n```bash\ncd model\npython evaluate_model.py\n```\n\nAlternatively, examine the training process:\n```bash\njupyter notebook model/train_model.ipynb\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add some amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## Acknowledgments\n\n- Amazon review dataset providers\n- The open source community for the amazing tools used in this project\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsecurdrgorp%2Fbigdata-amazon-reviews","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsecurdrgorp%2Fbigdata-amazon-reviews","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsecurdrgorp%2Fbigdata-amazon-reviews/lists"}