Which text feature do you use to find sentiment in textual data?

The term that accurately relates to the process of finding sentiment in textual data is text analysis. This feature specifically focuses on extracting insights and interpretations from text, which can include identifying the emotional tone or sentiment conveyed within the data. Text analysis encompasses various methodologies and techniques that can help assess opinions, attitudes, and emotions reflected in written content, making it well-suited for sentiment analysis tasks.

While full-text index allows for advanced searching capabilities within large texts, its primary function is not sentiment detection; rather, it enhances search efficiency. Fuzzy text search is used to locate matches that may not exactly correspond to the queried terms, useful in correcting typographical errors or variations in phrasing but does not pertain to understanding sentiment. Text mining, on the other hand, involves extracting relevant information from large volumes of text but is broader than just sentiment analysis and can include a variety of other text-processing tasks. Thus, text analysis is the most aligned with sentiment identification in textual data.