Sentiment Analysis is a natural language processing (NLP) technique used to determine whether a piece of text is positive, negative, or neutral. It's commonly used to analyze customer reviews, social media comments, and other forms of text data.

A Sentiment Analyzer uses machine learning models or lexicon-based approaches to analyze text and determine its sentiment. It processes the text to identify words, phrases, and context that indicate sentiment, then classifies the overall sentiment of the text.

Sentiment Analysis can be applied to various types of text, including social media posts, customer reviews, emails, feedback forms, survey responses, news articles, and more.

Yes, many Sentiment Analyzers are capable of handling multiple languages. However, the accuracy may vary depending on the language and the quality of the training data available for that language.