Soutify — Social Media Listening Platform
Backend Lead @ Soutify
The problem
Clients needed to know what was being said about them across Twitter and global news as it happened — in Arabic as well as English, a pair most listening tools handle poorly or not at all.
Constraints
- Thousands of posts per minute, continuously, without dropping mentions
- Arabic sentiment is not translated English sentiment — dialect and negation defeat naive models
- Users needed arbitrary Boolean queries over accounts, hashtags, keywords and time windows
Key decisions
Django for ingest and query chosen over a lighter async framework
the hard part was the query model, not the I/O. The ORM, migrations and admin carried a complex relational schema that would otherwise have been hand-written and hand-maintained.
Soutify helps clients track brand mentions, analyze public sentiment, and understand trending topics across multiple platforms simultaneously.
I designed and implemented the entire backend infrastructure using Django — a foundation that handles massive data streams while maintaining excellent performance. The core challenge: continuously monitoring Twitter feeds and global news sources without missing critical mentions or overwhelming servers.
I built the real-time monitoring system that lets users create custom search parameters — tracking specific users, hashtags, keywords, and complex Boolean queries across time periods. Working with the AI team, I integrated sentiment analysis that processes content in Arabic and English with nuanced sentiment scores and topic classification, handling thousands of posts per minute.
Technologies
Private client work — source code under NDA.
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