Sentiment Analysis is the use of software — increasingly AI — to automatically determine the emotional tone of text (positive, negative or neutral), used to understand opinion at scale.
Sentiment analysis reads text — reviews, social posts, support tickets, survey responses — and classifies the feeling behind it. Instead of reading thousands of mentions by hand, you get a measurable read on how people feel about your brand, product or campaign.
It powers social listening and brand health tracking: spotting a reputation issue early, measuring response to a launch or campaign, comparing sentiment against competitors (a qualitative side of share of voice), and surfacing themes in customer feedback at scale.
Automated sentiment struggles with sarcasm, context, mixed feelings and local language nuance, so treat scores as directional, not precise. Use it to spot trends and prioritise where to look — then read the actual mentions that matter. Modern AI models have improved accuracy but haven’t eliminated these limits.
Sentiment analysis uses software, increasingly AI, to automatically classify the emotional tone of text as positive, negative or neutral. It lets you understand opinion at scale across reviews, social posts, support tickets and surveys.
For social listening and brand health tracking — spotting reputation issues early, measuring reaction to launches and campaigns, comparing against competitors, and surfacing themes in customer feedback without reading every mention by hand.
It's directional rather than precise. Automated analysis struggles with sarcasm, context, mixed feelings and language nuance, so use scores to spot trends and prioritise, then read the mentions that matter. AI models have improved accuracy but not eliminated these limits.
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