🛫 Airline Sentiment Dashboard

Interactive Data Visualization & Sentiment Analysis Platform for US Airlines Twitter Data

🎉 Live Demo

The application is now deployed and live. Click the link above to explore the interactive dashboard!

📋 About

This is a modern, interactive web application built with Streamlit and Python that provides deep insights into customer sentiment across major US airlines through Twitter data analysis. The dashboard offers real-time sentiment visualization, geographic tweet distribution, airline comparison analytics, and word cloud generation—all in an intuitive, user-friendly interface.

Perfect for data analysts, business intelligence professionals, and anyone interested in social media sentiment analysis, this tool transforms raw tweet data into actionable business intelligence about customer satisfaction and airline performance.

✨ Key Features

🎯 Core Capabilities

📊 Sentiment Distribution Analysis
- Visualize tweet counts across positive, neutral, and negative sentiments
- Toggle between histogram and pie chart visualizations
- Real-time sentiment metrics and statistics
🗺️ Geographic & Temporal Analysis
- Interactive map showing tweet locations by time of day
- Hour-based filtering (0-23 hours)
- Identify peak tweeting patterns and geographic hotspots
✈️ Airline Breakdown Analytics
- Compare sentiment across 6 major US airlines (US Airways, United, American, Southwest, Delta, Virgin America)
- Multi-select airline comparison
- Faceted visualization by sentiment type
☁️ Word Cloud Generation
- Generate dynamic word clouds for each sentiment category
- Automatic filtering of URLs, usernames, and retweets
- Stop word removal for meaningful insights
📱 Random Tweet Display
- Explore individual tweets by sentiment category
- Understand real customer feedback and experiences

🖼️ Project Demo

Sentiment Distribution Visualization

Overview of tweet distribution across positive, neutral, and negative sentiments with interactive histogram and pie chart options

Geographic & Temporal Analysis

Interactive map showing tweet locations filtered by time of day for pattern identification

Airline Breakdown Comparison

Detailed comparison of sentiment distribution across different airlines with faceted visualizations

Word Cloud Analysis

Dynamic word cloud visualization highlighting the most frequent terms in each sentiment category

🚀 Getting Started

Prerequisites

Python 3.8 or higher
pip (Python package manager)

Installation & Local Setup

Clone the repository

git clone https://github.com/AbdulRehman393/streamlit-airline-sentiment.git
cd streamlit-airline-sentiment

Install dependencies
```
pip install -r requirements.txt
```
Run the application locally
```
streamlit run app.py
```
View in browser
- Streamlit will automatically open your browser to http://localhost:8501
- If not, manually navigate to that URL

📦 Project Structure

streamlit-airline-sentiment/
├── app.py                    # Main Streamlit application
├── requirements.txt          # Python dependencies
├── data/
│   └── Tweets.csv           # US Airlines Twitter sentiment dataset
├── images/                  # Project demo screenshots
│   ├── tweets_by_sentiments.png
│   ├── location_and_hour.png
│   ├── breakdown_airline_tweets.png
│   └── word_cloud.png
└── README.md               # Documentation

📊 Technologies & Libraries

Technology	Purpose
Streamlit	Web app framework for rapid UI development
Pandas	Data manipulation and analysis
NumPy	Numerical computing
Plotly	Interactive data visualizations
Matplotlib	Static plotting and figure creation
WordCloud	Text visualization and word frequency analysis

💡 How It Works

Data Processing Pipeline

Data Loading: CSV file is loaded from GitHub and cached using Streamlit's @st.cache_data decorator
Timestamp Parsing: Tweet creation times are converted to datetime format
Sentiment Analysis: Data is pre-classified as positive, neutral, or negative
Visualization: Multiple visualization types transform data into actionable insights

Interactive Dashboard Features

Sidebar Controls: Intuitive controls for filtering and toggling visualizations
Dynamic Filtering: Real-time data updates based on user selections
Responsive Layout: Automatically adapts to different screen sizes
Performance Optimization: Caching prevents redundant data processing

📈 Use Cases

Customer Experience Monitoring: Track airline customer satisfaction trends
Competitive Analysis: Compare sentiment across competing airlines
Crisis Detection: Identify sudden spikes in negative sentiment
Marketing Insights: Understand what customers are talking about on social media
Business Intelligence: Data-driven decision making for airline management

🌐 Deployment

Streamlit Cloud (Current Deployment)

This project is deployed on Streamlit Cloud and is live at:

https://app-airline-sentiment-8mwwconsnxvhqrfy6yhicz.streamlit.app/

To deploy your own version:

Push your code to GitHub
Visit Streamlit Cloud
Sign up with your GitHub account
Click "New app"
Select your repository: AbdulRehman393/streamlit-airline-sentiment
Set main file to app.py
Click "Deploy"

🔧 Customization

Modify Data Source

Update the DATA_URL variable in app.py:

DATA_URL = "https://raw.githubusercontent.com/your-username/your-repo/main/data/Tweets.csv"

Add More Airlines

Edit the airline selection in the sidebar:

choice = st.sidebar.multiselect('Pick Airlines', 
    ('Your Airlines Here'), key = 'airline_selection')

Adjust Visualizations

Customize Plotly and Matplotlib settings for different color schemes, sizes, and styles.

📝 Dataset Information

The application uses Twitter data about US Airlines including:

Airlines: US Airways, United, American, Southwest, Delta, Virgin America
Sentiment Labels: Positive, Neutral, Negative
Tweet Information: Text content, creation timestamp, airline reference, geographic location
Scale: Comprehensive dataset with thousands of classified tweets

📜 Course & Certification

This project was developed as part of the "Create Interactive Dashboards with Streamlit and Python" course on Coursera.

Learning Path: Data Analysis & Visualization
Duration: 2-hour hands-on project
Certification: View on Coursera

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to:

Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📄 License

This project is open source and available under the MIT License.

👨‍💻 Author

Abdul Rehman

GitHub: @AbdulRehman393
LinkedIn: in/khawaja-abdul-rehman-24088b266
Portfolio: Check out my other projects on GitHub

🙏 Acknowledgments

Data sourced from Twitter (now X) API
Dashboard built with Streamlit
Visualizations powered by Plotly and Matplotlib
Course: Create Interactive Dashboards with Streamlit and Python

📞 Support & Questions

If you have any questions or need assistance:

💬 Open an Issue
📧 Feel free to reach out

Made with ❤️ by Abdul Rehman

⭐ Star this repository if you found it helpful!

🚀 Try the Live App

Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
data		data
images		images
.gitignore		.gitignore
README.md		README.md
app.py		app.py
requirements.txt		requirements.txt

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