What is Sentiment Analysis?
Sentiment analysis is the process of classifying whether a block of text is positive, negative, or neutral. The goal that Sentiment mining tries to gain is to be analysed people’s opinions in a way that can help businesses expand. It focuses not only on polarity (positive, negative & neutral) but also on emotions (happy, sad, angry, etc.). It uses various Natural Language Processing algorithms such as Rule-based, Automatic, and Hybrid.
let’s consider a scenario, if we want to analyze whether a product is satisfying customer requirements, or is there a need for this product in the market. We can use sentiment analysis to monitor that product’s reviews. Sentiment analysis is also efficient to use when there is a large set of unstructured data, and we want to classify that data by automatically tagging it. Net Promoter Score (NPS) surveys are used extensively to gain knowledge of how a customer perceives a product or service. Sentiment analysis also gained popularity due to its feature to process large volumes of NPS responses and obtain consistent results quickly.
What is Sentiment Analysis?
Sentiment analysis is a popular task in natural language processing. The goal of sentiment analysis is to classify the text based on the mood or mentality expressed in the text, which can be positive negative, or neutral.
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