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Text mining and text analysis identifies textual patterns and trends within unstructured data through the use of machine learning, statistics, and linguistics. By transforming the data into a more structured format through text mining and text analysis, more quantitative insights can be found through text analytics.

Basic Methods Word frequency. Word frequency can be used to identify the most recurrent terms or concepts in a set of data. ... Collocation. Collocation refers to a sequence of words that commonly appear near each other. ... Concordance. ... Text Classification. ... Text Extraction. ... Machine Learning Algorithms. ... Cross-validation.

In general, text mining processes are comprised of several steps, such as information retrieval (usually performed by querying databases) followed by named entity recognition and finally information extraction.

Definition. Text mining is the art of data mining from text data collections. The goal is to discover knowledge (or information, patterns) from text data, which are unstructured or semi-structured. It is a subfield of Data Mining (DM), which is also known as Knowledge Discovery in Databases (KDD).

It is defined as "the process of discovering meaningful new associations, patterns and trends by mining a large amount of data stored in a warehouse".

Text mining is the process of extracting useful information from large unstructured data. The data source maybe papers, abstracts, HTML pages, or mails [2]. It uses the process of pattern recognition and lexical analysis for extracting information.

Text mining applications in the biomedical field include computational approaches to assist with studies in protein docking, protein interactions, and protein-disease associations.

Text mining is the process of exploring and analyzing large amounts of unstructured text data aided by software that can identify concepts, patterns, topics, keywords and other attributes in the data.