Sentiment Analysis, often called opinion mining, is a sub-field of Natural Language Processing (NLP) that analyzes textual data to discern the sentiment or emotional tone conveyed. It categorizes sentiments as positive, negative, or neutral based on the text's content and context. Sentiment Analysis is extensively used by businesses and organizations to gauge customer satisfaction, monitor brand reputation, and understand consumer perceptions.
Sentiment Analysis employs machine learning, statistical analysis, and linguistic rules to examine textual data like customer reviews, social media posts, and survey responses. By analyzing the language and expressions used in the text, it identifies and extracts subjective information to determine the sentiment towards particular topics, products, or services. Sentiment Analysis can be performed at different levels - document level, sentence level, or aspect level, each providing varying degrees of insight into the sentiment conveyed. The insights garnered from Sentiment Analysis help organizations respond to customer concerns, tailor marketing strategies, and make informed decisions. Despite its advantages, Sentiment Analysis can face challenges like detecting sarcasm, understanding context, and managing multilingual text analysis.