What is sentiment analysis in simple words?

Software reads a piece of text and decides whether the writer sounds positive, negative or neutral. Businesses use the result to sort reviews and messages quickly instead of reading each one by hand.

Can ChatGPT do a sentiment analysis?

Yes, a large language model can label text as positive, negative or neutral and explain why. For thousands of messages a day, it is usually wired into a workflow. See large language model for background.

What is an example of sentiment analysis?

A store scans its product reviews and finds that most complaints mention slow shipping. A support team marks angry emails as urgent so a person answers them first.

Is sentiment analysis still relevant?

Yes. Businesses still receive huge volumes of reviews, tickets and social posts, and nobody can read them all. Models handle context better than word lists, though sarcasm still trips them.

How accurate is sentiment analysis?

Accuracy depends on the text and the method. A study of reviews found one machine learning approach above 90% accuracy. Sarcasm and mixed reviews lower results in real use.

What Is Sentiment Analysis? Definition and Uses

Definition

Sentiment analysis

Sentiment analysis is a way of using AI to read text and label the feeling behind it as positive, negative or neutral. It is applied to reviews, emails and chat messages. That is what sentiment analysis is, in one sentence.

In short: AI that tells whether a message is happy, angry or neutral.

What is a sentiment analysis in practice

01
Text comes in
A review, email or chat message arrives.
02
The model reads it
AI looks at the words and their context.
03
A label is given
Positive, negative or neutral.
04
Teams act on it
Angry messages get attention first.

IBM describes it as the process of understanding and measuring how a customer feels about a product, service or brand.

IBM notes that it combines AI, machine learning and natural language processing to study customer feedback and reviews as they come in.

In simple words: software reads what people wrote and sorts it by mood. You do not have to read every message yourself.

NLP and sentiment analysis

Word lists

Count good and bad words
Miss context
Break on new phrasing

Machine learning

Learns from labeled examples
Reads words in context
Handles varied wording better

Sentiment analysis is one task within natural language processing, the field that lets computers work with human language.

Older systems counted positive and negative words from a list. Modern ones use machine learning, trained on many labeled examples, so they pick up context and not just keywords.

One study of product reviews labeled each as positive or negative. It found that a support vector machine approach reached over 90% accuracy, according to a paper published by MDPI.

This is natural language processing sentiment analysis: software reads the words and scores the tone.

Where sentiment analysis gets it wrong

Sarcasm
Praise words used to complain.
Mixed reviews
Love and anger in one message.
Slang
Jokes and jargon the model has not seen.
Hidden complaints
A calm tone can mask a problem.

Language is messy. Models stumble in predictable places.

A tool can misread tone, so it helps to know the weak spots before you rely on the scores.

Review sentiment analysis and other business uses

Good fit

Many reviews to scan
Support queues to sort
Trends to track over time

Poor fit

A few messages a month
Decisions that need no human check
Sarcasm-heavy feedback alone

The most common use is review sentiment analysis. It scans thousands of reviews to show what customers like and what keeps coming up as a problem. See review automation for collecting and answering reviews.

It also helps support teams. Messages that sound upset can be moved to the front of the queue, as covered in AI in customer service.

A person should still check the cases that matter most, which is the idea behind human in the loop.

Monitoring reviews across sites
Sorting support tickets by urgency
Tracking how customers feel about a launch
Spotting a drop in mood before customers leave

Our data

What AI answers when buyers ask

We asked ChatGPT, Gemini and Perplexity 10 buyer questions about review automation. They named 53 companies, and 68% appeared in only one of the three.

Criterion	ChatGPT	Gemini	Perplexity
Companies named	24	27	28
Sites cited most	podium.com (9 of 10)	thecmo.com (5 of 10)	zapier.com (5 of 10)

Nexum measurement, October 22, 2026, via DataForSEO: 10 buyer questions to each AI, US English, 30 answers in total. 9 companies appeared in all three. Google showed an AI Overview for 10 of the 10 questions.

FAQ

Sentiment analysis questions

Software reads a piece of text and decides whether the writer sounds positive, negative or neutral. Businesses use the result to sort reviews and messages quickly instead of reading each one by hand.
Yes, a large language model can label text as positive, negative or neutral and explain why. For thousands of messages a day, it is usually wired into a workflow. See large language model for background.
A store scans its product reviews and finds that most complaints mention slow shipping. A support team marks angry emails as urgent so a person answers them first.
Yes. Businesses still receive huge volumes of reviews, tickets and social posts, and nobody can read them all. Models handle context better than word lists, though sarcasm still trips them.
Accuracy depends on the text and the method. A study of reviews found one machine learning approach above 90% accuracy. Sarcasm and mixed reviews lower results in real use.

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