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.