Sentiment Analysis
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
An NLP technique that determines whether text expresses positive, negative, or neutral feeling, used to read customer reviews and social posts at scale.
What is Sentiment Analysis?
Sentiment analysis is a natural language processing technique that determines whether a piece of text expresses positive, negative, or neutral feeling. It turns opinions written in free text, like reviews, support tickets, and social posts, into numbers a company can count, track, and compare over time.
Under the hood it is usually a classification task: the model assigns each text to a sentiment class it learned from labeled examples.
Types of Sentiment Analysis
- Binary sentiment analysis sorts text into positive or negative, nothing in between.
- Multi-class sentiment analysis adds finer grades, such as neutral, or a one-to-five scale that mirrors star ratings.
- Aspect-based sentiment analysis scores each feature separately, so “great food, slow service” registers as positive on food and negative on service instead of canceling out.
Applications of Sentiment Analysis
- Customer feedback: condenses thousands of reviews into a satisfaction trend per product.
- Social media monitoring: tracks how sentiment toward a brand moves during a campaign or a crisis.
- Market research: reads consumer opinion from online discussion without running a survey.
Example of Sentiment Analysis
A hotel chain runs every guest review through an aspect-based model. The review “Perfect location, but the room smelled damp and check-in took forty minutes” produces three scores: location positive, cleanliness negative, service negative. A plain positive/negative classifier would flatten this into one muddy label.
Aggregated across thousands of reviews per property, the scores show each hotel exactly where it loses guests. The known weak spot is language itself: sarcasm like “Great, another cold shower” still fools models that read words literally.
Related AI terms: Classification · Natural Language Processing · Tokenization · Word Embedding
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About