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1 minute readReference note

What are the limits of automated sentiment analysis for executive reporting?

In brief

Automated sentiment analysis can help sort large text samples, but it needs validation against the type of content and decisions being assessed. A positive-or-negative label can miss sarcasm, mixed judgments, legal allegations, time references, and factual accuracy. Tone is not the same thing as reputational consequence.

The operational check uses human-reviewed examples with clear definitions and retains the underlying text. Errors should be examined by theme, entity, language, and severity where those differences matter. A neutral sentence describing a false criminal claim can carry more harm than strongly negative language about a minor product inconvenience.

A sentiment trend is only comparable while the sample and classification method remain sufficiently stable. Changing the classifier or prompt set can move the result without a new audience-facing event. The diagnostic output should show which claims drove the movement and whether they are accurate. That supports actual source work: correcting a false statement, adding missing context, or addressing a real issue. A color-coded score without examples can conceal precisely the information an ORM practitioner needs to decide what should change.

Sources

01
Towards a standard for identifying and managing bias in AI ↗National Institute of Standards and Technology
02
Generative Artificial Intelligence Profile, NIST AI 600-1 ↗National Institute of Standards and Technology
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