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alphaear-sentiment

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.

Published by RKiding ↗RKiding/Awesome-finance-skills

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Score the sentiment of one financial text

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Use the alphaear-sentiment skill's LLM-based analysis mode to analyze this financial text: `{text}`. Return only JSON with `score` from -1.0 to 1.0, `label` as `positive`, `negative`, or `neutral`, and a brief `reason`. Do not update any database.

Required inputs

  • `{text}`: the financial or news text to analyze

Suggested template by Skill Leaderboard, grounded in the linked source. Task evidence ↗

SKILL.md

name: alphaear-sentimentdescription: 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.

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.