Sentiment analysis, AKA  "opinion mining", is a technique used to analyze the emotions, attitudes, and opinions expressed in written or spoken language. The goal is to determine whether a particular piece of text is positive, negative, or "neutral" in tone.

Sentiment analysis works by using natural language processing (NLP) algorithms to identify specific keywords and phrases in the text that indicate a particular sentiment. These keywords and phrases are often pre-defined based on how people tend to express themselves in certain contexts.

Once the system has identified the relevant keywords and phrases, it applies a classification algorithm to determine whether the overall sentiment of the text is positive, negative, or neutral. The algorithm might use techniques such as machine learning or rule-based systems to make this determination.

While sentiment analysis is not perfect and can sometimes misinterpret the tone of a piece of text, it has become an increasingly useful tool for businesses, researchers, and others looking to understand the opinions and emotions of their customers, audiences, or users.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence