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Text mining, also known as text data mining, is the process of extracting meaningful insights from written resources with the application of advanced analytical techniques and deep learning algorithms. This process includes a Knowledge Discovery in Databases process, information extraction, and data mining.

Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis. Data mining techniques and tools enable enterprises to predict future trends and make more-informed business decisions.

“Text and data mining (TDM) is the process of deriving information from machine-read material. It works by copying large quantities of material, extracting the data, and recombining it to identify patterns.” – UK Government.

Widely used in knowledge-driven organizations, text mining is the process of examining large collections of documents to discover new information or help answer specific research questions. Text mining identifies facts, relationships and assertions that would otherwise remain buried in the mass of textual big data.

Textual data refers to any information or message conveyed in written or printed form. It can include everything from books, articles, and surveys to social media posts, chat conversations, and emails.

With NLP in data mining, computers can analyze text and voice data to derive meaningful insights. Some of the common NLP in data mining techniques include Sentiment Analysis, Named Entity Recognition, Text Summary, Topic Modeling, Keyword Extraction, and others.

Text processing refers to only the analysis, manipulation, and generation of text, while natural language processing refers to the ability of a computer to understand human language in a valuable way. Basically, natural language processing is the next step after text processing.