Sentiment analysis (also known as opinion mining or emotion AI) is the systematic identification, extraction, quantification, and study of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is commonly used in marketing, customer service, and clinical medicine to analyze voice of the customer materials such as reviews and survey replies, as well as online and social media and healthcare resources.

When it comes to studying sentiments, there are two main ways.

• A method based on a lexicon: It calculates a document's collective polarity by adding the polarities of individual words.

• An approach based on machine learning: The analysis is performed on a test dataset that contains a number of documents that must be categorized.

To make judgments, a model is built from a training dataset using powerful mathematical optimizations.

Uses of Sentiment analysis 

It's usually used to keep track of what people are saying about certain topics on social media. The capacity to derive insights from social data is a practise that many companies have adopted around the world. It can also play an important role in market research and customer service. With sentiment analysis, you can immediately learn about the customer's overall experience. As a result, sentiment analysis is quite useful to a marketer.