Extract Product Data from E-Commerce Sites
Extract product names, prices, descriptions, and images from e-commerce sites for analysis or catalog building.
Scrape Reviews for Sentiment Analysis
Scrape customer reviews from review sites and product pages for sentiment analysis and feedback insights.
Quick answer: Use the Web Scraper tool through ToolRouter to scrape reviews for sentiment analysis directly from Claude, ChatGPT, Microsoft Copilot, and OpenClaw — connect once, then drive it with plain-language prompts. No code required.
ToolCustomer reviews contain unfiltered feedback about what people love and hate about products and services. Analyzing reviews at scale reveals patterns that individual reading cannot — recurring complaints, feature requests, and sentiment shifts that correlate with product changes or competitor moves.
ToolRouter's scrape_page fetches review pages while extract_data pulls out individual review text, ratings, dates, and reviewer information. This structured review data becomes the foundation for sentiment analysis, topic modeling, and competitive benchmarking. You can track how sentiment changes over time, compare sentiment across competitors, and identify the specific issues driving negative reviews.
Product managers use review data to prioritize feature development. Marketing teams monitor brand perception. Customer success teams identify common pain points. Investors use review sentiment as a signal for company health. The raw data from reviews is one of the most honest signals available about product-market fit.
How to scrape reviews for sentiment analysis with Claude, ChatGPT, Microsoft Copilot, and OpenClaw
Connect ToolRouter
1Open connector settings Open Settings
2Add a custom connector with these details
Name
ToolRouterURL
https://api.toolrouter.com/mcp3Let Claude set you up Open Claude
Claude scrapes customer reviews and immediately helps you find patterns in the feedback. After extraction, ask Claude to identify recurring complaints, highlight what customers praise most, compare sentiment across products, or suggest product improvements based on the most common negative themes.
How to scrape reviews for sentiment analysis
Once connected (see setup above), use the Web Scraper tool — the same steps work with Claude, ChatGPT, Microsoft Copilot, OpenClaw, and any MCP client:
- Ask Claude: "Scrape the reviews page at example-store.com/product/123/reviews and extract review text, ratings, and dates"
- Claude fetches and extracts structured review data from the page
- Ask: "Analyze the sentiment of these reviews. What are the main themes in positive and negative feedback?"
- Claude provides a sentiment breakdown with key themes and actionable insights
Example prompt
Try this with the Web Scraper tool
Use the web-scraper tool to extract reviews from example-store.com/product/123/reviews. Then analyze the sentiment and tell me the top 3 complaints and top 3 praised features.
Tips
- Claude can perform sentiment analysis directly on the extracted review text
- Ask for a breakdown by star rating to see what each segment talks about
- Chain multiple review pages for comprehensive sentiment analysis
Frequently Asked Questions
How do I scrape reviews for sentiment analysis with an AI assistant?
Scrape customer reviews from review sites and product pages for sentiment analysis and feedback insights. Connect the Web Scraper tool to Claude, ChatGPT, Microsoft Copilot, and OpenClaw through ToolRouter, then ask the assistant in plain language. For example: Ask Claude: "Scrape the reviews page at example-store.com/product/123/reviews and extract review text, ratings, and dates" Claude fetches and extracts structured review data from the page
Which AI assistants can scrape reviews for sentiment analysis?
Claude, ChatGPT, Microsoft Copilot, and OpenClaw can all scrape reviews for sentiment analysis using the Web Scraper tool through ToolRouter, with no API keys or coding required.
What does the Web Scraper tool do?
Scrape, crawl, and extract structured data from any website with powerful web scraping tools.
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