Multi-Platform Sentiment Monitoring and Analysis

The Problem It Solves

Sentiment monitoring often involves collecting data from multiple social media and news platforms, but the platforms are fragmented with varying data formats, and timely analysis of sentiment and risks is crucial. Manual monitoring is time-consuming, error-prone, and risks missing key information, such as brand reputation decline or negative sentiment spread. Businesses or individuals need automated tools to track keywords, brands, or topics in real-time and generate structured reports to support decision-making.

How the Skill Works

Core Capabilities and Key Steps

The skill achieves multi-platform sentiment monitoring through the following process:
- Confirm Monitoring Parameters: The user provides parameters like keywords and time range; the tool defaults to the last 7 days and notifies the user to ensure the time range is explicitly specified.
- Multi-Platform Parallel Search: Using the web_search tool, it performs three batches of parallel searches totaling 12 rounds, covering 15+ platforms such as Weibo, Zhihu, Bilibili, Toutiao, news media, and social platforms. Search terms include year and month (e.g., 2026年4月), but after:/before: parameters are strictly avoided because they are unreliable in actual search tools; date control is implemented via manual verification after retrieving results.
- Detail Fetching: High-value links are fetched using web_fetch for full content details, prioritizing authoritative media reports and in-depth analysis articles, ensuring publication dates are within the time range.
- Sentiment Analysis and Structured Organization: Information is classified into positive, negative, neutral, or mixed sentiment, and risk levels (high, medium, low) are assessed based on factual data, avoiding subjective assumptions.
- Report Generation: A script generates an HTML visualization report, including sentiment overview, sentiment distribution charts, platform popularity rankings, and risk alert lists.

Scheduled Monitoring Mode

When the user specifies a time frequency (e.g., "every morning at 7 AM"), the skill can set up scheduled tasks. Using the scheduled_task_create MCP tool, it creates automatic monitoring tasks supporting multiple execution times and subsequent management (such as pausing, resuming, or deleting).

Applicability and Caveats

• Hard Constraint on Date Filtering: Out-of-range content must be discarded and not used to populate reports; dates are manually checked after search, and if a platform has no relevant content, it is noted as such.
• Search Round Control: Fixed at 12 rounds to prevent tool response slowdown, with OR combinations used for efficient coverage, which is sufficient for broad reach.
• Deduplication and Objectivity: For the same event across platforms, only the most detailed entries are retained; analysis is based on gathered information, and no unexisting features are fabricated.
• Technical Implementation: JSON data is generated using Python's dict and json.dump to avoid Chinese quotation mark issues; report generation relies on specified scripts.