Text mining, also known as text data mining or text analytics, is the process of deriving high-quality information from unstructured text data. It involves transforming unstructured text into structured data for easy analysis, using natural language processing (NLP) techniques to extract useful information and insights from large amounts of unstructured text data. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluating and interpreting the output. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, sentiment analysis, document summarization, and entity relation modeling.
Text mining is an automatic process that uses NLP to extract valuable insights from unstructured text. By transforming data into information that machines can understand, text mining automates the process of classifying texts by sentiment, topic, and intent. In a business context, unstructured text data can include emails, social media posts, chats, support tickets, surveys, etc. Sorting through all these types of information manually often results in failure. Not only because it’s time-consuming and expensive, but also because it’s inaccurate and impossible to scale. Text mining, however, has proved to be a reliable and cost-effective way to achieve accuracy, scalability, and quick response times.
Text mining is a component of data mining that deals specifically with unstructured text data. It involves the use of NLP techniques to extract useful information and insights from large amounts of unstructured text data. Text mining can be used as a preprocessing step for data mining or as a standalone process for specific tasks. It is a multi-disciplinary field based on data recovery, data mining, AI, statistics, machine learning, and computational linguistics.
Some common uses of text mining include screening job candidates based on the wording in their resumes, blocking spam emails, classifying website content, flagging insurance claims that may be fraudulent, analyzing descriptions of medical symptoms to aid in diagnoses, and examining corporate documents as part of legal discovery.
