Sentiment Analysis: Definition and Examples

Sentiment analysis, also called opinion mining, is a branch of natural language processing that aims to determine the emotional polarity of a text: positive, negative, or neutral. It relies on artificial intelligence models trained to recognize linguistic markers of emotion, judgment, and attitude in human language.

This technique goes far beyond simple binary classification. Modern sentiment analysis systems can detect subtle nuances such as irony, sarcasm, ambivalence, or mixed emotions. They are capable of analyzing sentiment at different levels of granularity: document level, sentence level, or even specific aspects of a product or service (aspect-based sentiment analysis).

In the context of prompt engineering, sentiment analysis is doubly relevant. On the one hand, large language models (LLMs) like Claude naturally excel at this task thanks to their deep understanding of language. On the other hand, understanding sentiment allows you to calibrate prompts to obtain responses adapted to the desired tone.

The applications are vast: brand reputation monitoring on social media, customer review analysis, detection of weak signals in user feedback, content moderation, or satisfaction analysis in customer support conversations. Companies heavily use this technology to transform large volumes of unstructured text data into actionable insights.