Sentiment Analysis Pipeline

Create a comprehensive sentiment analysis system.

Analysis requirements:
- Sentiment granularity: [BINARY/3-CLASS/5-CLASS/CONTINUOUS]
- Text source: [REVIEWS/SOCIAL MEDIA/SUPPORT TICKETS]
- Language: [LANGUAGE(S)]
- Aspect-based: [YES/NO]

Pipeline components:
1. Data preprocessing:
   - Text cleaning
   - Handling emojis/slang
   - Language detection
2. Model selection:
   - BERT-based classifiers
   - DistilBERT for efficiency
   - Multilingual models
3. Aspect-based sentiment:
   - Aspect extraction
   - Aspect-sentiment pairing
   - Opinion mining
4. Training:
   - Class balancing
   - Data augmentation
   - Domain adaptation
5. Evaluation:
   - Classification metrics
   - Sentiment distribution
   - Error analysis by category
6. Production:
   - Batch processing
   - Real-time API
   - Confidence thresholds
7. Insights:
   - Sentiment trends
   - Topic modeling
   - Dashboard visualization

Handle sarcasm and negation properly.