Build an n8n AI Agent for Sentiment Analysis

What a Sentiment Analysis Agent Does

The agent pulls text data from multiple sources—Intercom conversations, Trustpilot reviews, Twitter mentions, App Store reviews, survey responses—and runs each through an LLM for sentiment scoring. Every piece of feedback gets a sentiment label (positive, negative, neutral), a confidence score, and topic tags. Results flow into a dashboard, spreadsheet, or database for trend analysis.

The real value is not individual scores. It is pattern detection. When negative sentiment around "onboarding" spikes 40% in a week, you know something broke before your support queue tells you.

Architecture: LLM and Tools

A production sentiment analysis n8n workflow looks like this:

Data Collection: Multiple trigger nodes pull data on a schedule. A cron trigger fetches new Trustpilot reviews via API. A webhook receives Intercom conversation transcripts. An RSS node monitors social mentions. Each source feeds into a normalisation node that standardises the format: source, timestamp, text, author.

Sentiment Scoring: The normalised text hits an LLM node. The system prompt instructs the model to return structured JSON with sentiment, confidence, topics, and a one-line summary. For high-volume use cases (thousands of items per day), batch processing with a smaller model keeps costs manageable.

Storage and Alerting: Results are written to a Postgres or Airtable database. A separate n8n workflow runs every hour, queries recent scores, and calculates rolling averages by topic. When a topic's average sentiment drops below a threshold, a Slack alert fires to the product or support team.

This is a classic n8n use case for multi-source data aggregation. The n8n integrations library connects to most review platforms and support tools natively.

Example Prompt and Output

System prompt for the scoring node:

"Analyse the sentiment of the following customer feedback. Return JSON: {"sentiment": "positive|negative|neutral", "confidence": 0.0-1.0, "topics": ["..."], "summary": "..."}. Topics should be product areas like onboarding, pricing, performance, support, billing."

Given the review: "Love the product but the mobile app crashes every time I try to export. Been like this for weeks. Starting to look at alternatives."

{"sentiment": "negative", "confidence": 0.88, "topics": ["mobile app", "performance", "churn risk"], "summary": "User likes the core product but is frustrated by persistent mobile export crashes and considering alternatives."}

That "churn risk" tag triggers an immediate Slack notification to the customer success team.

Limitations and Edge Cases

Sarcasm is the classic failure mode. "Oh great, another update that breaks everything" reads as positive to some models. Include sarcasm examples in your prompt to improve detection, but expect imperfect results.

Mixed sentiment is common. A review that praises your product but criticises your pricing contains both positive and negative signals. Decide upfront whether you want overall sentiment or aspect-level scoring. Aspect-level is more useful but costs more tokens.

Language and cultural context matter. Sentiment expression varies dramatically across cultures. A factual, unemotional complaint in one culture might be a strong negative signal. If you serve a global audience, consider language-specific prompts.

Volume can be a problem. Processing 10,000 reviews per day through GPT-4 gets expensive fast. Use a smaller model for bulk scoring and reserve the larger model for edge cases where confidence is low. This tiered approach is a common n8n workflow pattern.

When to Hire an Agency

Basic sentiment scoring is a weekend project. But a production system that normalises data from six different sources, handles rate limits, deduplicates reviews, calculates rolling trends, and delivers actionable alerts requires proper engineering. If customer sentiment directly impacts your product roadmap or retention strategy, the accuracy and reliability of the system matter.

An n8n automation specialist can build a sentiment pipeline that scales with your data volume and integrates cleanly with your existing analytics stack.