Text analytics, also known as text mining or text data mining, is the process of deriving high-quality information from text using a combination of linguistic, statistical, and machine learning techniques. It involves modeling and structuring the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. Text analytics is used to process large volumes of unstructured text or text that does not have a predefined structure. It is a way of extracting valuable insights, trends, and patterns from unstructured text data that would otherwise be too resource and time-intensive to be analyzed manually by humans.
Some key features of text analytics include:
-
Machine learning: Text analytics combines a set of machine learning, statistical, and linguistic techniques to process large volumes of unstructured text.
-
Data visualization: Combined with data visualization tools, text analytics can help identify patterns and provide actionable insights.
-
Business intelligence: Text analytics is used to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data.
Text analytics is being used in various fields, including marketing, customer service, and research. It allows companies to find meaningful information across a variety of data sources, from customer support tickets to social media interactions.
