Text mining, sometimes referred to as text analytics, refers to the process of uncovering meaningful information from text. This information is typically derived through the understanding of patterns and trends in the data. Text mining usually involves the process of structuring the input, deriving patterns or learnings within the structured data, and then finally the evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty and interestingness. Typical text mining tasks include categorization, clustering, concept extraction, production of taxonomies, sentiment analysis, and entity relation modelling.
Text mining is used most often within our Loyalty Practice, where they have developed an extremely sophisticated library of inputs to help understand and evaluate the consumers loyalty towards a brand or product.