One-Off Company Review Scraper
Budget: $250 – $750 AUD
I need a scrape tol pull both company information and their customer reviews from a set of public websites I will supply. The job is a single, one-time scrape; once the data is gathered and verified, the project is complete.
Trust - P-i-l-o-t Site. 500 companies with the info and with all reviews from each company.
Info;
company_logo_url (string): “Mark Firth Online logo”
claimed_profile (bool): true (“Claimed profile” badge)
company_name (string): “Mark Firth Online”
industry_label (string): “Marketing Consultant”
alt_industry_label (string): “Consultant” (appears again in Company details)
reviewer_name (string): e.g., “Tariq Korejo”
reviewer_country_code (string): e.g., “IE”, “US”, “CA”, “JM”, etc.
reviewer_reviews_count (int): e.g., “• 2 reviews” (when shown)
review_date (date): e.g., “22 Oct 2025”
updated_date (date, optional): “Updated 22 Oct 2025”
rating_stars (int 1–5): e.g., 5
rating_text (string): “Rated 5 out of 5 stars”
verification_label (enum): Verified, Invited, or none
review_title (string): e.g., “Mark’s YouTube Program Exceeded My Expectations”
review_body (string/truncated): body + “See more”
company_reply_present (bool): e.g., “Company replied” (appears on one)
review_interactions
The extracted data should be compiled into a clean CSV file with clear column headers. Typical fields will include the company name, location, website, rating, review text, reviewer name (if available), and date. If a site requires pagination, scrolling, or basic anti-bot work-arounds, please handle that gracefully.
Deliverables:
- 500 companies and reviews from website
Trust - P-i-l-o-t Site. 500 companies with the info and with all reviews from each company.
Info;
company_logo_url (string): “Mark Firth Online logo”
claimed_profile (bool): true (“Claimed profile” badge)
company_name (string): “Mark Firth Online”
industry_label (string): “Marketing Consultant”
alt_industry_label (string): “Consultant” (appears again in Company details)
reviewer_name (string): e.g., “Tariq Korejo”
reviewer_country_code (string): e.g., “IE”, “US”, “CA”, “JM”, etc.
reviewer_reviews_count (int): e.g., “• 2 reviews” (when shown)
review_date (date): e.g., “22 Oct 2025”
updated_date (date, optional): “Updated 22 Oct 2025”
rating_stars (int 1–5): e.g., 5
rating_text (string): “Rated 5 out of 5 stars”
verification_label (enum): Verified, Invited, or none
review_title (string): e.g., “Mark’s YouTube Program Exceeded My Expectations”
review_body (string/truncated): body + “See more”
company_reply_present (bool): e.g., “Company replied” (appears on one)
review_interactions
The extracted data should be compiled into a clean CSV file with clear column headers. Typical fields will include the company name, location, website, rating, review text, reviewer name (if available), and date. If a site requires pagination, scrolling, or basic anti-bot work-arounds, please handle that gracefully.
Deliverables:
- 500 companies and reviews from website
Related categories:
JavaScript
Python
Data Processing
Web Scraping
Data Mining
Scrapy
Data Extraction
BeautifulSoup
Selenium
Automation