Multi-Platform Crawl & LLM Insights

@CauaAlves

Hello! I'm a Python developer specializing in web scraping, API integrations, and LLM-driven analytics, with experience building multi-platform crawlers for social media insights (e.g., sentiment from Twitter/Insta data). Understanding your needs: A compact script to crawl public posts from Twitter/Instagram/LinkedIn handles, normalize to JSON, and use LLM (OpenAI) for structured reports on sentiment, themes, and engagement. My plan: Libraries: Tweepy for Twitter v2, Instaloader/Selenium for Instagram, linkedin-api/unofficial for LinkedIn. Crawling: Fetch captions, hashtags, metrics, media URLs; fallback to headless Pyppeteer if APIs limited. Normalization: Common JSON schema with validation. LLM: OpenAI API calls with tailored prompts for insights; output JSON + report. Config: .env for keys/limits; README + Jupyter demo. Compliance: Public data only, rate limiting.

@merchants1

Hello, I can develop a structured Python-based crawler that collects public content from Twitter (X) and Instagram for merchant handles and generates a unified insight report powered by an LLM. ### Scope of Delivery • Public post collection (caption text, hashtags, engagement metrics, media URLs where accessible) • Unified normalized JSON schema across platforms • Clean modular Python architecture • .env-based configuration for API keys and rate limits • LLM-powered structured brand insight report including: * Sentiment trends (positive / neutral / negative) * Key thematic buckets * Engagement highlights and spike detection • Requirements file + clear README • Sample execution demonstrating JSON dump and AI-generated report All integrations will use official APIs or compliant public-data access methods. ### Timeline 14 days from project start. ### Budget ₹26,500 fixed. The system will be modular, extensible, and ready for future platform additions (including LinkedIn if API access is available). Looking forward to collaborating. Regards, Merchants.

@mhirforwork

Hi — this is a very clear use-case and a good fit for my work. I build lightweight data pipelines that crawl, structure and analyze public content instead of just dumping raw scraped data. For this project I would: • Crawl posts safely and consistently (rate-limited + stable selectors) • Normalize into a structured dataset (timestamps, engagement, text, metadata) • Prepare analysis-ready data instead of messy CSVs • Generate brand behaviour insights using LLM processing The goal would be a repeatable system — not a one-time scrape — so you can reuse it for more handles later. If useful, I can also keep the architecture simple so it runs locally without server costs. Happy to adjust depending on whether you prefer quick POC insights or a reusable research workflow.

@arpitasahasarkar

Hi, I can develop a structured Python-based crawler that collects public content from Twitter (X) and Instagram for merchant handles and generates a unified insight report powered by an LLM. ### Scope of Delivery • Public post collection (caption text, hashtags, engagement metrics, media URLs where accessible) • Unified normalized JSON schema across platforms • Clean modular Python architecture • .env-based configuration for API keys and rate limits • LLM-powered structured brand insight report including: * Sentiment trends (positive / neutral / negative) * Key thematic buckets * Engagement highlights and spike detection • Requirements file + clear README • Sample execution demonstrating JSON dump and AI-generated report All integrations will use official APIs or compliant public-data access methods. ### Timeline 7 days from project start. ### Budget ₹18,500 fixed. The system will be modular, extensible, and ready for future platform additions (including LinkedIn if API access is available). Looking forward to collaborating.

@hiruben

I build scrapers like this regularly. Crawled 400K+ pages with anti-bot bypass using Selenium and Playwright. For this I would use Tweepy for Twitter, Instaloader for IG, and headless browser for LinkedIn. Everything normalized into clean JSON then piped to OpenAI for analysis. Integrated LLM APIs in production. Can deliver prototype within a week.