Knowra Sentiment analysis Sentiment analysis Sentiment analysis uses computational methods to identify and classify emotional or evaluative content in text, such as positive, negative, or neutral attitudes.
Polarity classification : A text classification task that assigns positive, negative, or neutral sentiment. It is the simplest common form of sentiment analysis.
Customer experience management : The practice of measuring and improving how customers experience an organization. Organizations use sentiment in feedback to identify service problems and strengths.
Emotion recognition : The identification of specific emotions, such as anger, joy, or fear, from data. It distinguishes particular emotions rather than only evaluative polarity.
Natural language processing : The computational study and processing of human language. Sentiment analysis is one task within natural language processing.
Multilingual sentiment analysis : The analysis of sentiment in text written in multiple languages. Sentiment resources and model accuracy vary substantially across languages.
Aspect-based sentiment analysis : A method that identifies sentiment toward specific aspects or features mentioned in text. It separates opinions about different features within one passage.
Social media analytics : The collection and analysis of data from social media platforms. Sentiment analysis summarizes public reactions across social posts.
Subjectivity analysis : The classification of text as subjective expression or objective description. Subjectivity can be present without positive or negative sentiment.
Tokenization : The division of text into units such as words, subwords, or punctuation marks. Models need a representation of text before they can classify it.
Code-switching : The use of two or more languages or language varieties within a conversation or text. Mixed-language text complicates tokenization, context, and sentiment interpretation.
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