Fast TripAdvisor Hotel & Restaurant Reviews Scraper avatar

Fast TripAdvisor Hotel & Restaurant Reviews Scraper

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from $1.40 / 1,000 extracted fast tripadvisor hotel & restaurant reviews scrapers

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Fast TripAdvisor Hotel & Restaurant Reviews Scraper

Fast TripAdvisor Hotel & Restaurant Reviews Scraper

Scrapes hotel, attraction, and restaurant reviews from TripAdvisor: star ratings, review title, full text, author name, travel date, owner response, and photo attachments.

Pricing

from $1.40 / 1,000 extracted fast tripadvisor hotel & restaurant reviews scrapers

Rating

0.0

(0)

Developer

David Sandor

David Sandor

Maintained by Community

Actor stats

0

Bookmarked

2

Total users

1

Monthly active users

3 days ago

Last modified

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Fast TripAdvisor Hotel & Restaurant Reviews Scraper πŸš€

Scrapes hotel, attraction, and restaurant reviews from TripAdvisor: star ratings, review title, full text, author name, travel date, owner response, and photo attachments.

🌟 20+ Enterprise Enhancements (v2.0)

  • RAG & LLM Ready: Pre-computed OpenAI token counts and chunked embeddings.
  • Smart Keyword Filters: Include or exclude records by targeted keyword lists.
  • Sentiment Scoring: Built-in lexical sentiment rating on text contents.
  • Noise & Tracking Scrubber: Removes tracking query parameters and boilerplate banners.
  • Pay-Per-Event (PPE): Ultra-cost-effective pricing per extracted item.
  • Zero Cold Start: Sub-second execution with automated fallback guarantees.

πŸ’» Integration Examples

Node.js (Apify Client)

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('fast-tripadvisor-reviews-scraper').call({
// Pass customized inputs here
enableRagEnrichment: true
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log('Extracted Items:', items);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_APIFY_TOKEN')
run = client.actor('fast-tripadvisor-reviews-scraper').call(run_input={ 'enableRagEnrichment': True })
for item in client.dataset(run['defaultDatasetId']).iterate_items():
print(item)

πŸ“„ Output Schema

Returns structured JSON, token counts, and RAG vector chunks.