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

Capabilities

1. Analyze Sentiment (FinBERT / Local)

Use scripts/sentiment_tools.py for high-speed, local sentiment analysis using FinBERT.

Key Methods:

• 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)

For higher accuracy or reasoning capabilities, YOU (the Agent) should perform the analysis using the Prompt below, calling the LLM directly, and then update the database if necessary.

#### Sentiment Analysis Prompt

Use this prompt to analyze financial texts if the local tool is insufficient or if reasoning is required.

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}

Scoring Guide:

• 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)

Ensure DatabaseManager is initialized correctly.