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Plain-language AI glossary

Term 46ApplicationsMeaning / context / connections

Applications / Definition

Sentiment Analysis

An NLP technique that determines the emotional tone of text, classifying it as positive, negative, neutral, or more granular emotions.

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MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Sentiment Analysis works.

Sentiment analysis estimates attitudes or emotions expressed in text. A basic system classifies positive, negative, or neutral language, while a richer system can identify aspects, intensity, or specific emotions. Context, sarcasm, dialect, and domain language make the task less trivial than the labels suggest.

Language models can return structured sentiment labels, while smaller classifiers may be more economical at high volume. In either case, evaluate against human-labeled examples from the real audience and decide what action the signal should trigger. A score becomes useful when it supports routing, prioritization, research, or a measurable customer-response workflow.

02 / Practical uses

Where it helps.

  1. 01App store review monitoring
  2. 02Customer support escalation
  3. 03Brand perception tracking
  4. 04Content moderation
  5. 05Market research analysis
03 / Keep exploring
Natural Language Processing (NLP)The branch of AI focused on enabling computers to understand, interpret, and generate human language in useful ways.Named Entity Recognition (NER)An NLP technique that identifies and classifies named entities in text, such as people, organizations, locations, dates, and monetary values.Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.Prompt EngineeringThe practice of crafting effective instructions for AI models to produce desired outputs consistently.