Community sentiment monitoring pipeline

Challenge

With all engineering resources dedicated to developing the platform itself, the n8n team had limited capacity to build internal tooling. They partnered with Workfloows to deliver the solution.

The marketing team needed systematic visibility into how people discuss n8n across the internet: adoption patterns, feature sentiment, and recurring pain points. Until then, this required hours of manual monitoring across community channels and social platforms, with no structured way to track or compare findings over time.

The requirements were clear: start with Reddit as the primary source, but design an extensible architecture where additional platforms (X, LinkedIn, YouTube, etc.) could be connected later through a single unified data schema. All output had to flow into Notion databases, the tool their marketing team relies on daily for planning and reporting.

Solution

We built a workflow in n8n that runs periodically, searching Reddit for n8n-related posts and analyzing not just the main posts but entire comment threads beneath them. By processing full threads rather than individual mentions, the workflow captures broader context: how conversations evolve, what pain points keep resurfacing, and what users genuinely appreciate.

Each post and its comments are run through sentiment analysis and content categorization, then normalized into a unified schema and written to Notion databases. The marketing team gets a structured, up-to-date view of community sentiment without ever leaving the tools they already use.

The architecture was designed to be source-agnostic: Reddit is the first input, but adding X, LinkedIn, YouTube, or any other platform is a matter of plugging in a new source module that outputs the same schema.

Workflow

The workflow is triggered once a day and follows a clear pipeline:

1. Keyword search. The workflow searches Reddit for specified n8n-related keywords, retrieving relevant posts from targeted subreddits.
2. Deduplication. Retrieved posts are compared against records already stored in the database to avoid processing duplicates.
3. Comment parsing. For each new post, the full comment thread is fetched and parsed to fit within the AI context window, ensuring the analysis captures the complete discussion, not just the original post.
4. AI content analysis. An AI chain processes each thread and returns a summary, sentiment estimation, identified use cases, and applied tags (e.g. whether the described use case is AI-related).
5. Notion ingestion. The formatted, structured data is written to Notion databases where the marketing team can review, filter, and act on the findings.

Results

The workflow replaced hours of manual browsing with a fully automated, daily pipeline that delivers structured insights directly into the marketing team's existing Notion workspace.

The team now has continuous visibility into community sentiment without anyone dedicating time to manual tracking. Recurring themes, feature requests, and emerging issues surface automatically, giving the team a clear picture of how users perceive n8n and allowing them to respond faster when something needs attention.

With its source-agnostic architecture, the workflow is ready to scale as new platforms are connected, all feeding into the same unified view. And yes, it was built with n8n, making it a real example of the platform powering its own internal operations.