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 ## 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. ```markdown 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. ####...