---
name: AlphaEar Sentiment
slug: alphaear-sentiment
category: Data
description: AlphaEar Sentiment analyzes financial text and news to determine sentiment polarity and score using local FinBERT or an LLM prompt. Use it when you need positive, negative, or neutral labels with scores for market-related content.
github: "https://github.com/RKiding/Awesome-finance-skills/tree/main/skills/alphaear-sentiment"
language: Python
stars: 2781
forks: 362
install: "npx degit https://github.com/RKiding/Awesome-finance-skills/tree/main/skills/alphaear-sentiment ~/.claude/skills/alphaear-sentiment"
installs_to: ~/.claude/skills/alphaear-sentiment
source_path: skills/alphaear-sentiment/SKILL.md
collection_size: 10
category_size: 668
collection_url: "https://dirskills.com/collections/RKiding/Awesome-finance-skills"
added: 2026-08-17T07:10:26.215Z
last_synced: 2026-08-17T07:10:26.215Z
canonical_url: "https://dirskills.com/skills/alphaear-sentiment"
---

# AlphaEar Sentiment

AlphaEar Sentiment analyzes financial text and news to determine sentiment polarity and score using local FinBERT or an LLM prompt. Use it when you need positive, negative, or neutral labels with scores for market-related content.

**Install:**

```bash
npx degit https://github.com/RKiding/Awesome-finance-skills/tree/main/skills/alphaear-sentiment ~/.claude/skills/alphaear-sentiment
```

## README

# 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)

Ensure `DatabaseManager` is initialized correctly.