Overview
Built a real-time sentiment analysis system that monitors social media conversations and provides actionable insights on brand perception.
Key Features
- Multi-platform data collection (Twitter, Reddit, Facebook)
- BERT-based sentiment classification
- Real-time streaming analysis
- Topic modeling and trend detection
- Automated alert system for negative sentiment spikes
Technical Stack
- NLP: Transformers, BERT, SpaCy
- Data Collection: Tweepy, PRAW, Selenium
- Processing: Apache Kafka, PySpark
- Visualization: Streamlit, Plotly
Results
- 89% accuracy in sentiment classification
- Processes 10K+ messages per hour
- Detected brand crisis 3 hours before traditional monitoring
- Identified 5 key themes in customer feedback
Implementation
The system uses:
- Pre-trained BERT model fine-tuned on social media data
- Named Entity Recognition for brand mentions
- Aspect-based sentiment analysis for product features
- Time series analysis for trend detection
Deployed on AWS with auto-scaling to handle traffic spikes during marketing campaigns.