Analyze financial text with local FinBERT or an LLM to identify positive, negative, or neutral sentiment. Return a numeric score and short reason, with support for batch processing unanalyzed news using FinBERT.
Analyze finance text sentiment using FinBERT or LLM. Use when the user needs to determine the sentiment (positive/negative/neutral) and score of financial text markets.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM-based analysis modes.
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.
{'score': float, 'label': str, 'reason': str}.batch_update_news_sentiment(source, limit): Batch process unanalyzed news in the database (FinBERT only).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.
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:
update_single_news_sentiment(id, score, reason): Use this to save your manual analysis to the database.torch (for FinBERT)transformers (for FinBERT)sqlite3 (built-in)Ensure DatabaseManager is initialized correctly.
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