social-sentiment

What This Skill Does

The social-sentiment skill empowers you to perform enterprise-grade social listening by leveraging a massive index of over 1.5 billion posts across Twitter, Reddit, and Instagram. Unlike simple social media monitoring tools that provide limited insights, this skill facilitates large-scale data gathering, allowing you to export up to 64,000 posts per query into CSV format. By combining this bulk data with automated Python and pandas analysis, you can detect sentiment, identify emerging PR crises, contrast competitor reception, and extract granular themes from the full dataset rather than relying on small, unrepresentative samples.

Installation

To begin, ensure you have the Xpoz MCP configured. You can verify your connection by running mcporter call xpoz.checkAccessKeyStatus in your terminal. Once confirmed, install the skill via the OpenClaw repository: clawhub install openclaw/skills/skills/atyachin/brand-monitoring

Use Cases

• Brand Reputation Management: Monitor real-time public perception and identify spikes in negative sentiment before they evolve into full-blown crises.
• Competitive Benchmarking: Perform side-by-side sentiment analysis of your product against competitors to identify market gaps.
• Product Feedback Loops: Automatically aggregate user complaints and praises to prioritize your development roadmap based on actual user pain points.
• Event Impact Analysis: Gauge the reception of product launches, marketing campaigns, or corporate announcements as they unfold across social platforms.

Example Prompts

1. "Analyze the current sentiment for Notion over the last 30 days and list the top three common complaints found in the dataset."
2. "Compare the sentiment of Figma versus Canva users based on recent mentions and highlight why one has a higher engagement weight."
3. "Is there a spike in negative sentiment regarding the latest OpenAI release on Reddit? Please surface the core themes of the complaints."

Tips & Limitations

• Refine Your Queries: Use boolean operators in your search strings. For example, combine the brand name with keywords like 'slow', 'buggy', or 'best' to isolate high-value data points.
• Handle Data Volume: Because this skill processes tens of thousands of rows, ensure your local environment has sufficient memory when running complex pandas scripts.
• Stay Authentic: Always use the bulk export feature to avoid the 'sampling bias' common in other dashboarding tools; examining the full dataset ensures your findings are statistically significant.