social-sentiment

What This Skill Does

The social-sentiment skill empowers OpenClaw to perform comprehensive brand and product monitoring by aggregating data from Twitter, Reddit, and Instagram. By leveraging the Xpoz MCP integration, this skill accesses over 1.5 billion indexed posts to provide an unfiltered view of public opinion. Unlike simple sampling tools, social-sentiment enables bulk CSV exports of up to 64,000 rows per query, which the agent then processes using advanced Python and pandas automation. This allows users to conduct deep-dive analysis, identify viral PR crises, surface specific user complaints, and extract positive thematic trends at scale, turning raw social data into actionable business intelligence.

Installation

To begin, ensure your OpenClaw environment is configured with the Xpoz MCP. Install the skill directly via the command line:

clawhub install openclaw/skills/skills/atyachin/sentiment-analysis

After installation, verify your credentials by running mcporter call xpoz.checkAccessKeyStatus. If the returned status is hasAccessKey: true, you are ready to start your first analysis run. If not, follow the linked xpoz-setup documentation to authenticate.

Use Cases

• Brand Reputation Management: Monitor real-time sentiment shifts following a product launch or company announcement to detect and mitigate PR crises before they escalate.
• Competitive Benchmarking: Compare your brand’s sentiment against direct competitors by running parallel analysis queries and assessing thematic gaps.
• Product Feedback Loops: Isolate negative sentiment queries (e.g., "buggy," "slow," "frustrating") to identify top-tier complaints and product pain points that require immediate development attention.
• Trend Analysis: Gauge market reception of new events or industry-wide shifts by tracking sentiment fluctuations across large datasets over specific time periods.

Example Prompts

1. "Analyze the current public sentiment for Tesla across Twitter, Reddit, and Instagram, highlighting top complaints and praises."
2. "Compare sentiment for Notion vs Obsidian. Focus on which platform users find more reliable for long-term knowledge management."
3. "How are users reacting to the latest OpenAI announcement? Identify if the sentiment is trending negative due to privacy concerns."

Tips & Limitations

To maximize the effectiveness of your analysis, always use a multi-pronged query strategy. By segmenting your searches into direct brand mentions, explicit pain points, and enthusiastic praise, you generate a much clearer picture of the sentiment spectrum. Note that the skill relies on the Xpoz MCP; if the platform index is unreachable or the API rate limit is exceeded, the analysis might be throttled. Always handle large CSV exports in the agent’s working directory and ensure sufficient storage for high-volume data processing.