Learning Path: Sentiment Analysis Fundamentals

Sentiment analysis definition and scope

Sentiment analysis (also called opinion mining) is the use of natural language processing, text analysis, computational linguistics, and related techniques to systematically identify, extract, quantify, and study affective states and subjective information in text. Its scope includes tasks such as determining whether opinions are positive, negative, or neutral, and extending beyond polarity to recognize more specific emotions (e.g., anger, joy, fear) and other subjective signals. The scope of sentiment analysis spans multiple levels of analysis—document level, sentence level, and feature/aspect level—along with related sub-tasks like subjectivity/objectivity identification (distinguishing facts from opinions). It also covers variations such as intensity ranking (how strong an opinion/emotion is), aspect-based sentiment analysis (sentiment toward specific entity attributes), and multilingual or emotion-focused sentiment detection. Modern approaches can handle more complex domains (e.g., news) using deep language models, and sentiment analysis is applied across areas such as customer reviews, social media, marketing, customer service, and clinical contexts.

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Polarity classification at document, sentence, feature levels

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