Product Review Scraper for Sentiment Analysis

You are an expert data engineer specializing in web scraping and ETL pipelines for sentiment analysis.

Write a production-ready web scraper that collects public product reviews from {{target-site}} and outputs a clean, analysis-ready dataset. The scraper must extract:

- Review text (full body)
- Star rating
- Review date
- Verified purchase flag (boolean)
- Helpful vote count

**Privacy & compliance requirements:**
- Exclude all reviewer names, profile links, and personal identifiers
- Respect robots.txt and implement polite rate-limiting (minimum 1-second delay between requests)
- Handle authentication/session management if required by the site

**Pagination & reliability:**
- Page through all available reviews using the site's pagination mechanism (query parameters, infinite scroll, or "load more" buttons)
- Implement retry logic with exponential backoff for failed requests
- Log progress and allow resume from interruption
- Handle edge cases: reviews with no text, missing fields, partially loaded pages

**Data normalization:**
- Convert all star ratings to a consistent 0-5 scale (document the original scale and conversion logic)
- Parse dates into ISO 8601 format (YYYY-MM-DD)
- Normalize boolean fields (verified purchase) to true/false
- Cast helpful vote counts to integers, default to 0 if missing

**Cleaning pipeline:**
Include a separate cleaning function that processes the raw scraped data:

1. Strip all HTML/XML markup and decode entities
2. Collapse multiple whitespace characters (spaces, tabs, newlines) into single spaces
3. Trim leading/trailing whitespace
4. Drop reviews where text is empty after cleaning
5. Remove exact duplicates based on (text + rating + date)
6. Preserve original raw data in a separate column for audit purposes

**Output format:**
Save to {{output-format}} with schema: `review_text`, `rating_0_5`, `review_date`, `verified_purchase`, `helpful_votes`, `raw_text`. Include a summary report: total scraped, total after cleaning, duplicates removed, empty reviews dropped.

**Code requirements:**
- Use appropriate libraries (requests/httpx + BeautifulSoup/lxml for HTML, or Playwright/Selenium if JavaScript rendering required)
- Include clear docstrings and inline comments for site-specific logic
- Make selectors/XPath expressions easy to update when site structure changes
- Add error handling with informative messages

Provide the complete scraper script and cleaning module, plus brief setup instructions (dependencies, usage example).