--- language: en title: "Sentiment in AI" description: "Sentiment in AI classifies the tone of passages about a brand or attribute. Interpreting it requires context and a rubric; it does not measure consumers’ actual opinions." canonical: "https://tropk.ai/en/resources/glossary/sentiment-in-ai" last_modified: "2026-10-12T16:12:10.488Z" --- # Sentiment in AI Reviewed on 2026-10-06. ## What it means Sentiment in AI is the classification of the tone with which a response text describes a brand or an attribute. The analysis identifies favorable, unfavorable, or neutral passages according to a defined rubric. It concerns the language produced in that response, in its context—not an emotion attributed to the model or an opinion collected from consumers. ## Why it matters for marketing For marketing and communications, this classification helps identify praise and caveats that merit review. A text may praise the ease of signing up and criticize the response time. Separating attributes lets teams route each observation to the appropriate department, rather than treating a mixed assessment as full approval or assuming the criticism is factual. ## Example in practice Imagine a regional carrier described, hypothetically, as offering easy installation but slow support on holidays. The analyst classifies a passage about installation as positive and another about customer service as negative, while keeping the full response intact. Before proposing content changes, they request data on response times from the operations team: the negative tone flags an issue to investigate but does not prove that actual delays occurred. ## How to apply or verify Define the unit of classification: the entire response, a sentence, or a claim about an attribute. Specify how to handle negation, comparison, irony, and mixed assessments. Have people review a sample and record disagreements. In Profound, the help documentation consulted describes the extraction of claims, topics, and attributes; check the displayed passage and the configured analysis type before interpreting the dashboard. ## Important distinctions Sentiment and factual accuracy answer different questions: a complimentary sentence may contain incorrect information. The absence of a mention should not automatically be labeled neutral. The general taxonomy includes neutrality, while Profound’s Sentiment help highlights positive and negative polarities for attributes. Confirm which schema is being used; do not assume these approaches are equivalent. ## Sources and references - [The AI Search Glossary: A definition of 300+ terms]() — Profound - [About Sentiment]() — Profound ## Continue a leitura - [Brand perception in AI]() - [Sentiment prompt]() - [Aggregated sentiment score]() ## About the author [Luiz Gardelin]() Luiz Gardelin is the founder of Tropk.ai and works in GEO/AEO, product vision, strategy, and growth, helping brands understand and improve their presence in AI responses. --- [All pages in Markdown]()