Text analytics consist of the statistics about a text element, which includes the word count, the word histogram, and the word frequency histogram. Most text documents of value are related to other-sometimes many other-documents, and so analytics describing the relative frequency of terms in a document compared to its peers are important for defining key words (tagging, labeling, indexing), search-responsive terms (query terms), and compressed versions of the documents (key words, summary, etc.).This clearly written text explains the functional applications of search, translation, optimization, and learning with regard to text analytics. Generation of analytics is aided by a hybrid, ensemble, or other combinatorial approach in which two or more effective analytic processes are used simultaneously, and their outputs combined to form a better "consensus". Additional value to the preservation of the information is provided through these methods. Also, since they encompass capabilities of two or more knowledge-generating systems, they can create a "superset" of access points to the data generated. The book also describes the role of functional approaches in the testing and configuration of these systems.

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is 'meta' to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.