Large-Scale Social Media Sentiment Intelligence Platform

Year

2024

Tech & Techniques

🌊 Spark Streaming + Kafka Real-Time Pipeline🧠 Transformer Models (BERT, Multilingual BERT)🚀 Microservices Autoscaling Architecture📚 Topic Modeling + Sentiment Classification📊 Real-Time Dashboarding (BI Visualization)

Description

Jump to architecture

Processes millions of social media posts in real time to capture public sentiment shifts instantly.
Spark Streaming powers high-throughput ingestion and distributed text transformation.
Fine-tuned transformer models deliver context-sensitive sentiment and topic detection.
Kafka handles high-velocity data pipelines with guaranteed delivery.
Autoscaling microservices respond dynamically to social event spikes.
Dashboards provide real-time visibility into brand reputation and emerging topics.
Designed for global-scale workloads with multilingual capability.

Key Features

🌍 Multilingual sentiment + topic classification
⚡ Streaming data processing with fault tolerance
📈 Real-time trend monitoring and alerts
🔧 Microservices with autoscaling

Metrics

📊 2.5M+ posts/hour processing throughput.
📊 17% improvement in sentiment accuracy after custom BERT fine-tuning.
📊 <1 second end-to-end latency for streaming inference.
📊 28% cost reduction through intelligent autoscaling.

Tech Stack / Skills

Python, PySpark, KafkaBERT, HuggingFace TransformersDocker, KubernetesPower BI / Grafana dashboards

Interesting Highlights

📈 Detects viral trends before they peak.
🎯 Designed to handle unpredictable surge patterns such as political events or celebrity news.

Socials

Contact

Large-Scale Social Media Sentiment Intelligence Platform

Key Features

Metrics

Interesting Highlights