Text Mining: Approaches and Applications
The opportunities offered by today’s big data and unstructured information are a great impulse for companies to choose solutions based on text mining approaches and applications. Internal and external text content essential for business—customer service records, insurance claims, clinical trial data, as well as emails, news and social media content—is everywhere today. Text mining approaches and applications are essential for helping companies take advantage of this information for improving strategic business activities and boosting decision making.
Unlike data mining, which is designed to work with structured data, text mining (or text analytics) focuses on text-heavy business data and is intended to handle full-text documents, emails and web content. In other words, text mining handles the most relevant part of today’s enterprise business data that is stored as unstructured content in the form of text (over 80% of business data is unstructured).
Text mining approaches
The most common text mining approach involves a representation of text that is based on keywords. A keyword based methodology can be combined with other statistical elements (machine learning and pattern recognition techniques, for example) to discover relationships between different elements in text by recognizing repetitive patterns in present in the content. These approaches do not attempt to understand language, and may only retrieve relationships at a superficial level.
Text mining based on intelligent technologies such as artificial intelligence and semantic technology can leverage an understanding of language to more deeply understand a text. This enables extraction of the most useful information and knowledge hidden in text content and improves the overall analysis and management of information.
Text mining applications
As a powerful approach that improves a number of activities, from information management, data analysis and business intelligence to social media monitoring, text mining is successfully applied in a variety of industries. Here are some examples of the most used text mining applications:
Text mining supports organizations in managing unstructured information, identifying connections and relationships in information, and in extracting relevant entities to improve knowledge management activities.
Text mining helps companies to get the most out of customer data by capturing new needs and opinions from text to improve customer support through the ability to understand what clients are saying (for example via social media.)
Text mining extracts the entities that matter the most (people, places, brands, etc. and also relevant relationships, concepts, facts and emotions) from text. A better analysis of text means better results for entity extraction that can be integrated into other platforms and applications to improve business intelligence activities, for example.