Multi-Platform Social Sentiment Data Extraction

I need a robust yet lightweight solution that continuously gathers two kinds of information—user comments & posts and the hashtags or topic trends that surround them—so I can run reliable sentiment analysis afterwards. The sources in scope are Facebook, Twitter (X), Instagram, YouTube and Snapchat, both recent activity and a defined slice of historical data where accessible.

Here is what will make the project a success for me:

• A scraper or API-based pipeline (Python preferred, but I am open to Node or R) that respects each platform’s rate limits and terms of service
• Clean, deduplicated output in CSV or JSON with at least these fields: platform, date/time, raw text, hashtag list, basic engagement numbers, and an anonymised user ID
• Simple configuration file where I can add or remove keywords, hashtags or date ranges without touching the core code
• Clear documentation plus a short demo notebook or script showing how to load the data and run a sample sentiment check

If you have existing routines for natural language processing, Redis/SQL storage, or cloud deployment, feel free to weave them in as long as they keep the workflow portable. Code review and a quick run on my own machine will serve as the final acceptance test.