AI-Powered Review Analysis and Clustering from Trustpilot
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Automate the extraction, processing, and AI-driven clustering of Trustpilot reviews for in-depth customer feedback analysis using n8n and Langchain. This workflow transforms raw review data into actionable insights.
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About This Workflow
Overview
This n8n workflow is designed to automate the process of gathering customer reviews from a specified company on Trustpilot, processing them, and then using AI to cluster them for sentiment and topic analysis. It addresses the challenge of manually sifting through large volumes of customer feedback by leveraging web scraping, data transformation, and advanced AI models. The core logic involves fetching review data, cleaning and structuring it, generating embeddings, storing these embeddings in a vector database (Qdrant), and then using AI to identify clusters of similar reviews.
Why use this workflow?
- Efficiency: Automates a time-consuming manual process of data collection and analysis.
- Scalability: Can handle large volumes of reviews from any company on Trustpilot.
- Deeper Insights: Utilizes AI (Langchain, OpenAI embeddings) to uncover hidden patterns and themes within customer feedback.
- Structured Data: Transforms unstructured review text into a structured format suitable for further analysis or RAG applications.
Problem it solves:
The primary problem this workflow solves is the difficulty in deriving meaningful, actionable insights from a high volume of customer reviews. Manually reading, categorizing, and analyzing feedback is inefficient and prone to bias. This workflow provides an automated, data-driven approach to understand customer sentiment, identify common issues, and discover positive trends.
Key Features
- Web Scraping: Extracts review data (author, rating, text, date, country, etc.) from Trustpilot using the
Extract Reviewsnode. - Data Transformation: Cleans and structures raw extracted data into a usable format with the
Zip EntriesandReviews to Listnodes. - AI-Powered Document Loading: Prepares review text and associated metadata for AI processing using
Default Data LoaderandRecursive Character Text Splitter. - Embeddings Generation: Creates vector representations of review text using OpenAI's embedding models (
Embeddings OpenAI) for semantic understanding. - Vector Database Integration: Stores review embeddings in Qdrant for efficient similarity search and retrieval.
- AI Clustering: Uses OpenAI's chat models (
OpenAI Chat Model) to cluster reviews based on their semantic content, identifying common themes and sentiments.
How To Use
- Configure Initial Settings: In the
Set Variablesnode, specify thecompanyIdby replacing the placeholder URL with the actual Trustpilot URL of the company you want to analyze. - Set Date Range: In the
Set Variables1node, define thereview_date_fromandreview_date_toparameters to specify the period for which you want to retrieve reviews. Defaults to the current month. - Trigger Workflow: Click the 'Test workflow' button on the
When clicking ‘Test workflow’node to initiate the process. - Review Extracted Data: Monitor the output of the
Extract Reviewsnode to see the raw review data being scraped. - Process and Embed Data: The subsequent nodes will automatically process the data, generate embeddings, and store them in Qdrant.
- Analyze Clusters: The final nodes will perform AI-based clustering. You can then inspect the output of the
Only Clusters With 3+ pointsnode to see the identified review clusters and their associated points.
Apps Used
Workflow JSON
{
"id": "d59980c0-e469-40ba-b481-70cf69ef958f",
"name": "AI-Powered Review Analysis and Clustering from Trustpilot",
"nodes": 0,
"category": "AI Research & Data Analysis",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: d59980c0-e469...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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