📱 Sentiment Analysis on Mobile Phone Reviews

A comprehensive sentiment analysis platform designed to help businesses, analysts, and product managers gain actionable insights from mobile phone reviews. Leveraging advanced natural language processing (NLP) and interactive visualizations, this tool empowers users to understand customer sentiment, identify product strengths and weaknesses, and make data-driven decisions.

🎥 Demo Video

✨ Key Features

• Upload & Analyze Reviews: Seamlessly upload CSV files containing mobile phone reviews for instant sentiment analysis.
• Aspect-Based Sentiment Analysis: Detect positive or negative sentiment for specific features like battery, camera, or display.
• Customizable Analytics: Filter results by phone model, brand, or review month for tailored insights.
• Interactive Visualizations: Explore sentiment trends with bar charts, pie charts, and line graphs.
• PDF Export: Generate and download visually consistent reports for sharing or offline use.
• Debug Data: View raw and processed data for transparency and troubleshooting.

📊 Analytics & Insights

• Feature-wise Sentiment: Understand which features (e.g., battery, camera, performance) are most praised or criticized.
• Monthly Sentiment Trends: Track sentiment changes over time for specific phones or brands.
• Overall Sentiment: Get a high-level view of positive vs. negative feedback per product.
• Brand & Product Comparison: Compare sentiment across brands and models to identify market leaders.
• Sentiment by Rating: Analyze correlations between star ratings and sentiment.
• Sentiment by Verified Purchase: Differentiate between verified and unverified reviews for authenticity.
• Demographic Analytics: Explore sentiment by age, gender, or platform (if data is available).
• Best Features & Areas to Improve: Identify top strengths and weaknesses based on review percentages.

💡 Why It Matters for Businesses

• Product Improvement: Pinpoint features that delight or frustrate customers to guide R&D.
• Competitive Benchmarking: Compare your products against competitors to highlight unique selling points.
• Customer-Centric Decisions: Base decisions on real customer feedback, beyond star ratings.
• Marketing Insights: Highlight top features in campaigns and address pain points proactively.
• Trend Monitoring: Detect sentiment shifts to respond swiftly to market changes or issues.

🛠️ Technologies Used

Backend

• Python (Flask/FastAPI): Powers API endpoints and data processing.
• NLP Libraries: Custom aspect-based sentiment analysis using spaCy, NLTK, or transformers.

Frontend

• Next.js (React + TypeScript): Modern, fast, and scalable web application.
• Tailwind CSS: Responsive, dark-themed UI for a sleek user experience.
• Recharts: Interactive and customizable data visualizations.
• react-select: Advanced dropdowns for intuitive filtering.
• jspdf & html2canvas: Seamless PDF report generation.

🚀 Getting Started

Follow these steps to set up and run the project locally:

1. Clone the Repository:

   git clone https://github.com/ayaz9616/B-Vision.git

2. Install Dependencies:
   • Backend:

     cd server
     pip install -r requirements.txt

   • Frontend:

     cd frontend
     npm install

3. Run the Backend:

   cd server
   python app.py

4. Run the Frontend:

   cd frontend
   npm run dev

5. Access the App:

   Open http://localhost:3000 in your browser.

📂 Project Structure

Sentiment-Analysis-on-reviews/
├── server/           # Python backend (API, NLP processing)
├── frontend/         # Next.js frontend (UI, visualizations)
├── uploads/          # Storage for uploaded review files
├── public/           # Static assets (images, etc.)
└── README.md         # Project documentation

📝 Example Use Cases

• Product Managers: Identify features to enhance in the next phone release.
• Marketers: Highlight top-rated features in campaigns.
• Customer Support: Address recurring issues proactively.
• Analysts: Benchmark sentiment across brands and time periods.

🌙 Dark Theme & Accessibility

• Dark Theme: All UI elements, including loading, error, and no-data states, feature a modern dark background for an eye-friendly experience.
• Responsive Design: Optimized for seamless use on desktops, tablets, and mobile devices.
• Accessibility: Built with a focus on usability and inclusivity.

🤝 Contributing

We welcome contributions! To get started:

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

Please report Almeida or suggest features by opening an issue.

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgements

• Open-source communities for NLP libraries (spaCy, NLTK, transformers) and React/Next.js.
• Inspiration from real-world needs for product review analytics.

📬 Contact

For questions, suggestions, or support, please open an issue or contact the maintainer.

Empower your business with actionable insights from real customer voices! 🌟