Sentiment Analysis
Definition
Sentiment analysis detects indications of positive, negative, or other subjective attitudes from texts.
The automatic classification of subjective attitude or polarity in texts, possibly related to specific aspects.
Simply explained
The system reads feedback and estimates whether someone sounds satisfied, dissatisfied, or rather neutral.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
Customer comments are grouped into positive, neutral, and negative.
Limits and pitfalls
- Ignoring irony, domain shifts, and mixed statements or equating sentiment with customer satisfaction.
Origin & history
Sentiment Analysis evolved from Opinion Mining and text classification starting in the early 2000s.
Use in mid-sized companies
- Possible application: Customer comments are grouped into positive, neutral, and negative.
Frequently asked questions
What does sentiment analysis mean?
The automatic classification of subjective attitude or polarity in texts, possibly related to specific aspects. What mistakes should be avoided? Ignoring irony, domain shifts, and mixed statements or equating sentiment with customer satisfaction.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: