Analytics

Social Sentiment Intelligence

BrandWatch Agency

Python Claude API D3.js
7 platforms unified
The Problem

The Challenge

BrandWatch Agency was managing social presence for 12 clients across 7 platforms, with no unified way to track sentiment, identify crises early, or report to clients with consistent, actionable data.

Quick Stats
7
Platforms Unified
12
Client Brands Monitored
94%
Sentiment Classification Accuracy
What We Built

The Solution

We built a sentiment intelligence platform that collects posts, comments, and mentions across Twitter/X, Instagram, TikTok, Facebook, LinkedIn, Reddit, and Google Reviews. A Python scraping layer feeds into a classification pipeline powered by the Claude API for nuanced sentiment analysis — distinguishing irony, cultural context, and brand-specific tone.

D3.js powers an interactive front-end where BrandWatch's team can drill into sentiment trends, topic clusters, and influencer maps. Crisis alerts fire automatically when sentiment spikes negatively, with Slack and email notifications.

Daily executive summary PDFs are auto-generated for each client at 8am, pulling the previous day's highlights and trend commentary.

Python Claude API D3.js PostgreSQL Redis Celery FastAPI
Impact

Key Results

7
Platforms Unified
12
Client Brands Monitored
94%
Sentiment Classification Accuracy
Gallery
Project Screenshot 1
Project Screenshot 2
Project Screenshot 3

"We used to spend 3 days a week compiling reports. Now it's automated, more accurate, and our clients love the dashboards."

PN
Priya Nair
Analytics Lead, BrandWatch Agency
Next Project

Neural Retail Engine

AI Development

View Case Study
Work With Us

Ready to Build Something Like This?

Tell us your challenge. We'll design the solution.

Start a Project