Alphaear Sentiment

AlphaEar Sentiment Skill

Overview

This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM-based analysis modes.

Capabilities

1. Analyze Sentiment

Use scripts/sentiment_tools.py to analyze short texts like news titles or summaries.

Key Methods:

• analyze_sentiment(text): Get sentiment score and label.
  • Returns: {'score': float, 'label': str, 'reason': str}.
  • Score Range: -1.0 (Negative) to 1.0 (Positive).
• batch_update_news_sentiment(source, limit): Batch process unanalyzed news in the database.

Example Usage (Python):

from scripts.database_manager import DatabaseManager
from scripts.sentiment_tools import SentimentTools

db = DatabaseManager()
# mode="auto" selects FinBERT if available, else LLM (mock/api)
sentiment_tools = SentimentTools(db, mode="auto")

# Analyze single text
result = sentiment_tools.analyze_sentiment("Company X reports record-breaking profits for Q4.")
print(result)
# Output: {'score': 0.95, 'label': 'positive', 'reason': '...'}

Dependencies

• torch (for FinBERT)
• transformers (for FinBERT)
• sqlite3 (built-in)

Ensure DatabaseManager is initialized correctly.