Analytics Updated 2026-07-04

Sentiment Analysis

Sentiment Analysis is the automated classification of text to determine whether it expresses positive, negative, or neutral sentiment. AI models process language to infer the emotional tone or opinion in user content, reviews, or mentions.

Definition

Sentiment analysis tools use natural language processing to evaluate text from reviews, social media posts, comments, and customer feedback. The models assign sentiment scores or categorical labels to each piece of content. Techniques range from rule-based keyword matching to trained deep learning models that understand context and nuance.

Marketers apply sentiment analysis to monitor brand perception, identify emerging issues, and measure campaign impact. The technique reveals how audiences perceive products, campaigns, or companies at scale. Results often surface unexpected patterns, such as a campaign being received differently across geographies or demographics.

Why it matters for AI visibility

Sentiment analysis of AI responses mentioning a brand reveals how AI assistants characterize products or companies. Negative sentiment in AI-generated text directly shapes user perception in ways that differ from traditional media. Teams monitoring AI sentiment can identify and address inaccurate or unfavorable characterizations in AI responses.

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