3. Sentiment Analysis Engine

NLP Processing Pipeline

The Sentiment Analysis Engine implements state-of-the-art natural language processing methodologies utilizing transformer architectures for multi-platform sentiment analysis. The system employs sophisticated preprocessing techniques and context-aware sentiment scoring.

Model Architecture

BERT_CONFIG = {
    'model_type': 'finbert-sentiment',
    'max_sequence_length': 512,
    'batch_size': 32,
    'embedding_dim': 768,
    'attention_heads': 12,
    'transformer_layers': 6
}

Preprocessing Pipeline

The system implements advanced text normalization and feature extraction:

class NLPProcessor:
    def __init__(
        self,
        language_model: str = "finbert-sentiment",
        min_confidence: float = 0.75,
        cache_size: int = 10000
    ):
        self.sentiment_classifier = pipeline(
            "sentiment-analysis",
            model=language_model
        )
        self.nlp = spacy.load("en_core_web_sm")
        self.crypto_lexicon = self._load_crypto_lexicon()
        self.pattern_windows = defaultdict(
            lambda: deque(maxlen=1000)
        )

Multi-Platform Data Aggregation

The social scraper implements rate-limited API interactions with multiple platforms:

Platform Configuration

Rate Limits:
    twitter:
        requests_per_minute: 60
        max_results_per_request: 100
    telegram:
        requests_per_minute: 30
        batch_size: 50
    discord:
        requests_per_minute: 50
        message_history_limit: 100

Cache Configuration:
    tweet_cache_duration: 900  # seconds
    user_cache_duration: 3600  # seconds
    sentiment_cache_duration: 300  # seconds

Embedding Model Architecture

The system utilizes custom embedding models for crypto-specific sentiment analysis:

Model Parameters

EMBEDDING_CONFIG = {
    'vocab_size': 50000,
    'max_position_embeddings': 512,
    'hidden_size': 768,
    'intermediate_size': 3072,
    'num_attention_heads': 12,
    'num_hidden_layers': 6,
    'type_vocab_size': 2
}

Feature Extraction Pipeline

async def _analyze_sentiment_signal(
    self,
    window_data: List[MarketCondition]
) -> float:
    """Analyze sentiment signal in time window.
    
    Args:
        window_data: List of market conditions
        
    Returns:
        Float sentiment signal score
    """
    sentiment_scores = [d.sentiment_score for d in window_data]
    weights = np.linspace(0.5, 1.0, len(sentiment_scores))
    weighted_sentiment = np.average(sentiment_scores, weights=weights)
    
    return float(weighted_sentiment)

Performance Characteristics

Processing Metrics:
    Throughput: 100+ texts/second
    Latency: <50ms per inference
    Batch Processing: 32 samples/batch
    Memory Usage: ~4GB RAM per instance

Model Performance:
    Accuracy: >0.85 for sentiment classification
    F1 Score: >0.82 for multi-class prediction
    ROC-AUC: >0.88 for binary classification

Error Handling

The system implements sophisticated error recovery mechanisms:

ERROR_HANDLING = {
    'max_retries': 3,
    'backoff_factor': 2,
    'timeout': 30,
    'circuit_breaker': {
        'failure_threshold': 5,
        'reset_timeout': 60
    }
}

Monitoring and Metrics

The engine exposes detailed performance metrics:

Prometheus Metrics:
    - sentiment_analysis_duration_seconds
    - embedding_generation_time
    - cache_hit_ratio
    - api_request_latency
    - model_inference_time

Alert Configurations:
    - HighLatencyAlert: >100ms processing
    - LowAccuracyAlert: <0.8 confidence
    - APIFailureAlert: >5% error rate
    - ResourceExhaustionAlert: >90% memory

Data Quality Assurance

QUALITY_THRESHOLDS = {
    'min_text_length': 10,
    'max_text_length': 1000,
    'min_confidence': 0.7,
    'language_detection_threshold': 0.9,
    'spam_probability_threshold': 0.8
}

The system maintains strict data quality standards through automated validation and filtering mechanisms, ensuring high-quality input for sentiment analysis processing.