Interactive dashboard analyzing customer sentiment across major US airlines using Twitter data. The project demonstrates data analysis, sentiment visualization, and temporal pattern recognition using Python and Streamlit.
- Real-time filtering by airline
- Sentiment analysis visualization
- Temporal pattern analysis (hourly & weekly trends)
- Interactive metrics and charts
- Tweet length analysis by sentiment
- Highest tweet volume occurs during business hours (peak at 9 AM)
- Negative tweets tend to be longer in length, suggesting detailed customer complaints
- Southwest Airlines shows the highest positive sentiment ratio
- Weekend activity shows different patterns compared to weekdays
- Python
- Pandas for data processing
- Streamlit for dashboard
- Plotly for visualizations
- Ngrok for local deployment
- Source: Twitter US Airline Sentiment (Kaggle)
- Size: 14,487 tweets
- Period: February 2015
- Airlines: American, Delta, Southwest, United, US Airways, Virgin America
- Real-time Twitter data integration
- Sentiment prediction model
- Geographic analysis
- Competitor comparison features