README.md

metadata

title: Sentiment-Analysis
emoji: 📊
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false
app_port: 7860

📊 End-to-End MLOps Pipeline for Sentiment Analysis regarding Online Reputation

🚀 Project Overview

MachineInnovators Inc. focuses on scalable, production-ready machine learning applications. This project is a comprehensive MLOps solution designed to monitor online company reputation through automated sentiment analysis.

Unlike standard data science experiments, this repository demonstrates a full-cycle ML workflow, moving from model training to automated deployment. It addresses the business need for real-time reputation tracking by classifying social media feedback (Positive, Neutral, Negative) using an automated pipeline.

Key Features

• Production-First Approach: Focus on scalability, modularity, and code quality.
• CI/CD Automation: Integrated pipeline for automated testing and deployment using GitHub Actions.
• Continuous Deployment: Automatic deployment to Hugging Face Spaces upon successful builds.
• Reproducibility: Code and environment are strictly versioned to ensure consistent results.

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🛠️ Tech Stack & Tools

• Core: Python 3.9+
• Machine Learning: [FastText / Transformers (RoBERTa)] **
• MLOps & CI/CD: GitHub Actions
• Deployment: Hugging Face Spaces
• Version Control: Git
• Development: Google Colab (Prototyping) -> VS Code (Production)

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⚙️ Architecture & MLOps Workflow

The project follows a rigorous MLOps pipeline to ensure reliability and speed of delivery:

1. Data Ingestion & Preprocessing:

   • Cleaning and tokenization of social media data using industry-standard libraries.
   • Usage of public datasets labeled for sentiment analysis.

2. Model Development:

   • Implementation of a robust sentiment classification model.
   • Optimization for inference speed and accuracy.

3. CI/CD Pipeline (GitHub Actions):

   • Linting: Enforces code style (PEP8) to maintain high readability.
   • Testing: Unit tests ensure that data processing and prediction logic function correctly before any merge.
   • Delivery: Upon passing all checks on the main branch, the application is packaged and deployed.

4. Deployment:

   • The model is served via a web interface hosted on Hugging Face Spaces, allowing for immediate user interaction and testing.

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📂 Repository Structure

├── .github/workflows/  # CI/CD configurations (GitHub Actions)
├── app/                # Application code (Inference & UI)
├── src/                # Source code for training and processing
│   ├── model.py        # Model architecture and training logic
│   ├── preprocess.py   # Data cleaning pipeline
│   └── utils.py        # Utility functions
├── tests/              # Unit and integration tests
├── notebooks/          # Exploratory Data Analysis (EDA) and prototyping
├── requirements.txt    # Project dependencies
└── README.md           # Project documentation

Clone the repository:

Bash

git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name
Install dependencies:

Bash

pip install -r requirements.txt
Run the application:

Bash

python app/main.py
# OR if using Streamlit/Gradio
streamlit run app/app.py
Run Tests:

Bash

pytest tests/
📈 Results and Performance
Model Accuracy: [Insert Accuracy, e.g., 85%]

F1-Score: [Insert F1 Score]

Inference Speed: [Optional: e.g., <50ms per tweet]

Note: Detailed analysis of the model's performance and the confusion matrix can be found in the notebooks directory.

🔮 Future Improvements
Drift Detection: Implementing tools like Evidently AI to visualize data drift.

Containerization: Fully Dockerizing the application for cloud-agnostic deployment (AWS/GCP).

API Expansion: Creating a REST API using FastAPI for integration with external dashboards.

🤝 Contributing
Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

📝 License
Distributed under the MIT License. See LICENSE for more information.

💡 Note for the Reviewer
This project was developed as a comprehensive exercise to demonstrate Full-Stack Data Science capabilities, bridging the gap between model development and production engineering.