How AI sentiment analysis works
Sentiment analysis tells you how the writer feels about something: positive, negative, neutral or mixed. Reading a handful of reviews is easy, but reading thousands is not, so teams use sentiment analysis to spot unhappy customers early and see whether opinion is moving up or down.
How the model decides
Older tools added up positive and negative words, so "not good" could score as positive because it contains "good". A language model reads the whole sentence, understands negation, comparisons and context, and returns one of four labels: positive, negative, neutral or mixed. Mixed is used when the text clearly contains both praise and criticism, such as "great food, terrible service".
Each result carries a confidence score. A low score means the text is ambiguous, very short or written in a way that is hard to read.
Aspect-based sentiment
A single label hides detail. In "the room was clean but the staff were rude", the room is positive and the staff are negative. Enter up to five aspects, such as price, service or quality, and the tool reports the sentiment toward each one, so you know what to fix.
Where it helps
Use it to triage support messages, track reviews over time, watch social comments after a launch, compare feedback before and after a change and find the topics that make customers happiest or angriest.
Known weak spots
Sarcasm ("great, another delay"), slang, emojis used ironically and very short texts are hard for every sentiment tool. It works best in English and handles many other languages with less certainty. Treat the label as a guide and read the messages that matter.
Analysing reviews in bulk
This page checks one text at a time. For a spreadsheet of reviews or a live feed, use the sentiment analysis API, which has a free monthly quota on every plan.