Real-Time Sentiment Analysis

Tech Stack

The Problem

• Binary positive/negative sentiment tools were too coarse to capture the emotional nuance brands needed.
• Existing platforms could not classify text across multiple emotional dimensions simultaneously.
• Standard models failed on short informal text such as tweets, reviews, and customer feedback messages.
• No real-time pipeline processed multi-label sentiment at the speed required for live brand monitoring.
• Single-label classifiers could not accurately handle complex mixed-emotion content common in online discourse.

Our Solution

• Built a multi-label sentiment classifier covering multiple emotional dimensions with independent scoring.
• Implemented a robust text preprocessing pipeline handling informal language, abbreviations, and HTML entities.
• Trained ensemble classifiers to independently predict confidence scores across each emotional dimension.
• Created a real-time inference API capable of streaming text classification at production-grade throughput.
• Designed an analytics dashboard visualising multi-dimensional sentiment distributions for brand monitoring teams.

Impact

Enabled granular brand sentiment monitoring with multi-dimensional emotional analysis.