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nikada / Getty Images Text analytics, sometimes called text data mining, is the process of uncovering insightful and actionable information, trends, or patterns from text. The extracted and structured data is much more convenient than the original text, making it easier to determine the information’s data quality and usefulness. Developers and data scientists can then use the mined data in downstream data visualizations, analytics, machine learning, and applications. Text analytics aims to identify facts, relationships, sentiments, or other contextual information. The types of information extracted often start with tagging entities such as people’s names, places, and products. It can advance to assigning topics, determining categories, and discovering sentiments. When measures such as currencies, dates, or quantities are extracted, establishing their relationship to other entities (and any qualifiers) is a key text analytics capability.

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