social-sentiment

What This Skill Does

The Social Sentiment skill is an enterprise-grade social listening and brand intelligence tool designed for the OpenClaw AI agent. It enables users to tap into a massive index of over 1.5 billion social media posts across major platforms including Twitter, Reddit, and Instagram. By leveraging bulk data exports and custom Python analysis, this skill allows you to move beyond surface-level metrics. It identifies emerging PR crises, compares your brand against competitors, and quantifies public opinion by processing up to 70,000 posts per operation. Whether you are tracking a new product launch or managing long-term brand reputation, this tool automates the aggregation and interpretation of raw sentiment data.

Installation

To install this skill, ensure your OpenClaw environment is configured correctly. Run the following command in your terminal: clawhub install openclaw/skills/skills/atyachin/social-sentiment

After installation, you must initialize the environment by running the setup utility: mcporter call xpoz-setup Verify your credentials by executing mcporter call xpoz.checkAccessKeyStatus to ensure you have active API access to the underlying social data streams.

Use Cases

• Brand Monitoring: Track real-time shifts in public perception for your company or specific products.
• PR Crisis Detection: Set up automated triggers for negative keyword clusters (e.g., "broken," "buggy") to alert your team before an issue goes viral.
• Competitive Intelligence: Compare sentiment scores between your brand and your biggest competitors to identify market gaps.
• Product Feedback Loops: Aggregate thousands of user posts to surface common feature requests or recurring UI/UX complaints that aren't visible through support tickets alone.

Example Prompts

1. "Analyze the last 30 days of sentiment on Twitter for 'Notion' and provide a summary of the top negative themes regarding their performance."
2. "Compare the sentiment score of our brand vs our main competitor on Reddit and export the results to a CSV file for my Q3 report."
3. "Check if there has been a recent spike in viral negative posts mentioning 'login error' on Instagram and report the top 5 most engaged posts."

Tips & Limitations

• Data Handling: Always store your analyzed datasets within the data/social-sentiment/ directory to track historical trends over time.
• Data Depth: Reddit data is generally considered more candid and detailed; prioritize it for deep-dive qualitative analysis.
• Scale: The skill supports bulk exports up to 64,000 rows. Use the provided Python template to filter out noise; sentiment is only as good as the keyword dictionary you define.
• Limitations: API response times depend on current platform load; always poll the checkOperationStatus endpoint after initiating a long-running data dump to prevent system timeouts.