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Sentiment analysis identifies and studies affective states and subjective information in text, often using NLP and computational methods.
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.
Sentiment analysis identifies and studies affective states and subjective information in text, often using NLP and computational methods.
It operates at multiple granularities (document, sentence, feature/aspect) and can go beyond polarity to emotions, subjectivity/objectivity, and sentiment intensity.
Common scope extensions include aspect-based sentiment analysis, intensity ranking, and emotion/multilingual detection, with applications across business, social media, and healthcare.
The use of NLP and related techniques to identify, extract, quantify, and study affective states and subjective information in text.
An alternative name for sentiment analysis, focused on extracting opinions from language data.
Classifying a text’s expressed sentiment as positive, negative, or neutral at the document, sentence, or feature/aspect level.
Classifying text as objective (factual) or subjective (opinionated) to support sentiment analysis and related tasks.
Determining sentiment toward specific entity attributes or aspects (e.g., food quality vs. service) rather than the overall text.
Estimating how strong an emotion or sentiment is within a text, beyond just labeling it as positive or negative.
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