An AI-powered tool that evaluates news headlines to provide sentiment classification, intensity scores, and confidence levels.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install news-sentiment-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install news-sentiment-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is News Sentiment Analyzer?
The News Sentiment Analyzer is a specialized component within the ecosystem of Openclaw Skills designed to provide automated emotional analysis of news content. It allows developers and researchers to programmatically determine whether news headlines or articles are positive, negative, or neutral. By leveraging advanced language models, this skill translates raw text into actionable data points, including sentiment scores and confidence metrics, making it easier to quantify the emotional pulse of the media.
Built for efficiency, this skill supports batch processing, allowing users to analyze multiple strings of text in a single execution. It integrates seamlessly with the SkillPay system, providing a low-cost, pay-per-use model for high-quality sentiment analysis. Whether you are tracking market trends or monitoring brand reputation, this skill simplifies the process of extracting sentiment from large volumes of news data.
News Sentiment Analyzer Use Cases
- Financial market analysis by monitoring news sentiment to predict potential price movements.
- Public relations monitoring to track brand perception across various media outlets.
- Content curation and filtering to highlight positive industry developments or flag negative news.
- Academic research and data mining for sentiment trends in historical news archives.
How News Sentiment Analyzer Works
- The user submits a JSON object containing an array of news texts or headlines to the skill.
- The skill processes the input using AI models to identify the underlying emotional tone of each entry.
- Each text is assigned a sentiment label (POSITIVE, NEGATIVE, or NEUTRAL), a sentiment score, and a confidence percentage.
- The processed data is returned in a structured JSON format for easy integration into other applications.
- Payment is automatically handled through SkillPay, charging 0.001 USDT per analysis call.
News Sentiment Analyzer Setup
To use this skill within your Openclaw Skills environment, you can optionally configure your own API key for enhanced control:
# Set your OpenAI API key (optional)
openclaw config set OPENAI_API_KEY your_secret_key
# Run the analyzer with a set of news headlines
openclaw run news-sentiment-analyzer --texts '[\"Fed signals interest rate cuts\", \"Market crashes on news\"]'
News Sentiment Analyzer Data Schema & Taxonomy
The skill returns a structured response for every text analyzed. Below is the metadata taxonomy for the results:
| Property | Type | Description |
|---|---|---|
text |
String | The input news headline or article snippet. |
sentiment |
String | The emotional classification: POSITIVE, NEGATIVE, or NEUTRAL. |
score |
Float | The intensity of the sentiment, typically between 0 and 1. |
confidence |
Float | The AI model's certainty level regarding the analysis (0 to 1). |
News Sentiment Analyzer Advanced Features
- Batch Analysis: Process multiple news headlines simultaneously to reduce latency in high-volume environments.
- SkillPay Integration: Benefit from a transparent micro-transaction model for cost-effective AI utility.
- Custom Provider Support: Use your own secret keys to manage rate limits and model preferences independently.
- Precise Confidence Metrics: Use the confidence score to filter out ambiguous results for higher data reliability.
SKILL.md
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