Sentiment & Social Listening

The Conversation Is Already Happening

A quick-commerce brand in India launches a new 10-minute delivery promise. Within hours, Twitter erupts. Some users celebrate. Others post screenshots of deliveries that took 40 minutes. A Reddit thread titled "Is [brand] lying about delivery times?" gains 500 upvotes. Instagram comments on the brand's launch post are 60% negative. By the time the marketing team checks their inbox Monday morning, the narrative has already been set.

This happens every week in India's hyper-connected digital market. Brands are discussed, praised, mocked, and boycotted in real-time across platforms — whether or not the brand is listening. AI-powered social listening means you are never caught off-guard again. You know what is being said, where, by whom, and whether it is getting better or worse — in minutes, not days.

Why Social Listening Matters for Indian Brands

The Scale Problem

An average Indian D2C brand with 100K+ customers generates:

No human team can read, categorize, and respond to all of this. AI can. It reads everything, flags what matters, and surfaces patterns invisible to manual monitoring.

The Speed Problem

In India, a negative customer experience can go viral in hours — not days. A single tweet with a bad delivery photo, tagged to the brand and a few influencers, can generate 10,000 impressions before lunch. AI detects sentiment spikes in real-time, alerting your team before a complaint becomes a crisis.

Where Indians Talk About Brands

Sentiment Analysis Basics

What AI Measures

Sentiment analysis categorizes text into positive, negative, or neutral — but good AI goes deeper:

The Sentiment Score

AI assigns a score from -1.0 (extremely negative) to +1.0 (extremely positive). Most brand mentions cluster between -0.3 and +0.5. What matters:

Open data/social-mentions.json in the code panel. This file contains 30 days of simulated social mentions for a D2C brand — with platform, text, sentiment score, emotion tags, and influence scores. Practice identifying patterns before working with your own data.

Handling Hinglish and Mixed-Language Content

The Challenge

Standard sentiment analysis tools trained on English fail spectacularly on Indian social media:

The Fix

AI models trained on or fine-tuned for Indian languages handle this better. When using general AI (like Claude or ChatGPT) for sentiment analysis:

Crisis Detection

Early Warning Signals

AI monitors for these patterns that indicate a brewing crisis:

Indian Crisis Patterns

Indian social media crises have unique characteristics:

Competitive Intelligence

AI does not just listen to your brand mentions — it monitors competitors too:

What to Track

Building a Competitive Dashboard

Ask AI to monitor 3-5 competitors weekly. Key metrics:

Open data/sentiment-trends.csv to see 90 days of comparative sentiment data — your brand vs two competitors — broken down by week, platform, and topic category. Notice where sentiment diverges and what caused it.

A Social Listening Workflow

Key Takeaways