alphaear-sentiment▌
rkiding/awesome-finance-skills · updated Apr 8, 2026
This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM-based analysis modes.
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).
- Returns:
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.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
alphaear-sentiment is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Piyush G· Sep 9, 2024
Keeps context tight: alphaear-sentiment is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Registry listing for alphaear-sentiment matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sakshi Patil· Jul 7, 2024
alphaear-sentiment reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend alphaear-sentiment for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Oshnikdeep· May 5, 2024
Useful defaults in alphaear-sentiment — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Apr 4, 2024
alphaear-sentiment has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Rahul Santra· Mar 3, 2024
Solid pick for teams standardizing on skills: alphaear-sentiment is focused, and the summary matches what you get after install.
- ★★★★★Pratham Ware· Feb 2, 2024
We added alphaear-sentiment from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yash Thakker· Jan 1, 2024
alphaear-sentiment fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.