AlphaEar Sentiment Skill

Overview

Capabilities

Capabilities

1. Analyze Sentiment (FinBERT / Local)

scripts/sentiment_tools.py

• analyze_sentiment(text)

  : Get sentiment score and label using localized FinBERT model.
  • 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 (FinBERT only).

2. Analyze Sentiment (LLM / Agentic)

Sentiment Analysis Prompt

请分析以下金融/新闻文本的情绪极性。
返回严格的 JSON 格式:
{"score": <float: -1.0到1.0>, "label": "<positive/negative/neutral>", "reason": "<简短理由>"}

文本: {text}

• Positive (0.1 to 1.0): Optimistic news, profit growth, policy support, etc.
• Negative (-1.0 to -0.1): Losses, sanctions, price drops, pessimism.
• Neutral (-0.1 to 0.1): Factual reporting, sideways movement, ambiguous impact.

Helper Methods

• update_single_news_sentiment(id, score, reason)

  : Use this to save your manual analysis to the database.

Dependencies

• torch

  (for FinBERT)

• transformers

  (for FinBERT)

• sqlite3

  (built-in)

DatabaseManager