Sentiment Analysis

The core problem sentiment analysis solves is simple: you have thousands of customer interactions every day—reviews, social media comments, support tickets, survey responses—and you can't possibly read them all yourself. Manually sorting through this feedback is slow, expensive, and inconsistent. Different people interpret tone differently. Sentiment analysis automates this process.

Think of it like a focus group moderator who never sleeps. Just as a moderator listens to what customers say and picks up on frustration, enthusiasm, or indifference in their voice, sentiment analysis reads written text and identifies the emotional undertone. It assigns a score: positive (customer is happy), negative (customer is upset), or neutral (factual statement). Some advanced systems also detect mixed sentiment ("I love the product but hate the shipping cost").

In practice, you'll see sentiment analysis in marketing tools like social listening platforms, customer feedback aggregators, and review monitoring software. When you use a tool like Brandwatch or Sprout Social, the AI is running sentiment analysis on every mention of your brand. It tells you not just how many people are talking about you, but whether they're saying good things or bad things. Similarly, e-commerce platforms use it to flag negative reviews automatically so you can respond quickly.

For CMOs, the practical implication is this: sentiment analysis turns unstructured customer feedback into actionable data. Instead of reading 500 support tickets, you get a dashboard showing "78% positive sentiment this week, up from 72% last week." You can identify emerging problems (sudden spike in negative sentiment about a product feature) or celebrate wins (campaign drove 15% increase in positive mentions). When evaluating AI tools, ask whether sentiment analysis is included and whether it integrates with your existing customer data sources.