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.
Best use case
alphaear-sentiment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using alphaear-sentiment should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/alphaear-sentiment/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How alphaear-sentiment Compares
| Feature / Agent | alphaear-sentiment | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.
#### 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)
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