The problem
Flying blind on community health
Stacks' Telegram community was active, multilingual, and impossible to monitor manually. Complaints repeated, sentiment shifted, and there was no free tool that could surface any of it.
So I built one.
What I built
End-to-end, no engineers required
01
Designed the full system architecture independently with Cursor, deciding on a local Python pipeline for privacy with a hosted web viewer for easy sharing.
02
Built a multilingual NLP pipeline using HuggingFace sentiment models (a Korean-specific model and a standard English model) processing Telegram JSON exports locally.
03
Designed and built the full dashboard in HTML/JS with four views covering top complaints, sentiment trends over time, and top community voices.
04
Built and deployed a public illustrative demo using 90,000 synthetic messages, so the tool can be shared without exposing real user data.
Python
HuggingFace Transformers
Google Sheets API
HTML / CSS / JavaScript
Render
Telegram JSON export
Outcomes
What it delivered for my team
Early crisis signals
Caught sentiment spikes after protocol events before they became community issues.
Event impact visibility
Tracked how announcements and launches moved community sentiment.
Repeat complaints surfaced
Identified recurring friction points that would have gone unnoticed.
Proactive strategy
Acted on data rather than reacting to complaints after the fact.
Bilingual coverage
Monitored Korean and English channels with equal accuracy.
Zero cost
Replaced the need for a paid sentiment tool with a free alternative.
How to use it
Try it for yourself!
Run the tool with your own community data. It runs locally. Your messages stay on your machine.
1
Download from GitHub
Clone the repo or download as a ZIP.
2
Install Python dependencies
Requires Python 3.9+.
3
Export your Telegram chat
Use Telegram Desktop to export as JSON.
4
Run sentiment analysis
First run downloads the AI model (~250MB).
5
Open the dashboard
Start the local server or connect via Google Sheets.
6
Explore your data
Browse Dashboard, Trends, and Complaints.
Full setup guide on GitHub