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Reviews & Q&A

Three scrapers for review platforms and question-and-answer communities. Pull structured reviews and answer threads without fighting markup changes.

Overview

Reviews and Q&A pages carry some of the highest-signal user-generated content on the web — verified product feedback, lived-experience answers, and the language real customers use to describe problems. These scrapers turn that content into JSON you can pipe into LLMs, sentiment models, or product-research dashboards without scraping each domain by hand.

Common use cases:

  • Product research: surface real pros/cons from g2-product-reviews for a category you're entering or a competitor you're sizing up.
  • VoC analytics (voice of customer): pipe verified-buyer reviews into a sentiment model and segment by reviewer-role / company-size to find which customer profiles love or hate a feature.
  • Content discovery: walk quora-question threads to find the exact phrasing buyers use when describing the problem your product solves — input for landing-page copy and SEO.
  • AI training: build a retrieval index of high-quality Q&A pairs for an answer engine or domain-specific copilot.

Both scrapers handle pagination internally — a single G2 product call returns the rating distribution and the visible review batch; pass page for older pages. Quora threads return the question + all loaded answers in one shot.

G2

G2 software-product reviews. Returns the overall rating, total review count, rating distribution, and the parsed reviews — including reviewer role and company size where G2 surfaces them.

  • G2 Product Reviews — software-product reviews on G2 — rating distribution, individual reviews, pros/cons.

Quora

Quora question pages — the question, all answers, author info, and engagement counts.

Example call

Below: a single g2-product-reviews call. The response carries the product-level aggregates plus a parsed review batch — title, rating, reviewer role, and company size.

curl 'https://api.crawlbase.com/?token=YOUR_TOKEN' \
  --data-urlencode 'url=https://www.g2.com/products/zoom/reviews' \
  --data-urlencode 'scraper=g2-product-reviews' -G

Sample response

{
  "product_name": "Zoom",
  "overall_rating": 4.5,
  "total_reviews": 52840,
  "reviews": [
    {
      "title": "Reliable for daily standups",
      "rating": 4.5,
      "reviewer_role": "Engineering Manager",
      "reviewer_company_size": "51-200 employees"
    }
  ]
}

Full reference (parameters, all 4 SDK languages, edge cases):G2 Product Reviews — full reference