I need a clean, well-structured dataset of customer opinions on a range of skincare products pulled exclusively from Facebook and Twitter. Your task is to programmatically extract public posts, comments, and replies where users discuss or review specific products or brands that I will supply once we start. What I expect • A CSV (or similar) containing at least these columns: platform, date/time, author handle, post URL, full text, basic engagement metrics (likes, shares/retweets, replies), and the product or brand keyword hit. • Simple, reproducible code (Python preferred—Scrapy, Tweepy, Facebook Graph API, or other reliable libraries) and a short README so I can rerun it later. • All data must be gathered in compliance with each platform’s terms of service; no login-required or private content. Scope details – Focus on customer reviews and experience-sharing posts—avoid ads or brand-authored content unless a user is commenting there. – Platforms in scope are Facebook and Twitter; if you’re able to add Instagram later we can discuss that as a follow-on. – Initial target is 5 k+ distinct reviews, balanced across the two networks, collected within the last 12 months unless a product has limited volume. Acceptance criteria 1. Sample of 100 records supplied within two days for validation. 2. Final dataset passes a quick manual spot-check for relevance and correct field population. 3. Scripts run end-to-end on my machine without modification beyond path variables. If this fits your skill set and you can deliver rapidly, let’s get started.