Text mining also known as text data mining is the process of exploring and examining
large collections of unstructured text data assisted by software to generate new information and to
transform the unstructured text into structured data for further analysis.
Text analytics, an application enabled by the use of text mining techniques to sort through data sets. Text analytics allows the organization to find potentially useful business insights in professional documents, customer emails, call center logs, verbal survey comments, social media platform posts, medical records and other sources of text-based data.
Typical text mining tasks involve text categorization, text clustering, and concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
Text Analytics Use Case
Companies deploy AI chat bots and virtual agents that use text analytics to provide automated responses
to customers as part of their marketing, sales and customer service working operations.
Text mining use case involves screening job candidates based on the wording present in their resumes, blocking spam emails, categorizing website content, flagging insurance claims that may be fraudulent, examining descriptions of medical symptoms to help in diagnosis, and examining corporate documents as part of electronic discovery processes.
Text mining can also assist to predict customer churn, helping companies to take action to head off potential defections to business rivals as part of their marketing and customer relationship management programs.