A class of NLP techniques that classify text or speech along an emotional dimension — positive, negative, neutral — and increasingly along finer-grained axes like confidence, frustration, or engagement. In meeting copilots, sentiment analysis powers features like "the customer's tone shifted negative at minute 23" or "this stakeholder seemed disengaged for the second half of the call." Sentiment is harder than it looks: sarcasm, deadpan humor, and cultural communication norms (a Dutch participant's bluntness is not anger) all confound naive models. The most useful applications of sentiment analysis in meetings are private and reflective — surfacing patterns to the speaker themselves rather than scoring participants for managers — because the latter creates a chilling effect where people perform emotions for the algorithm.