Overview of content related to 'text mining'
This page provides an overview of 1 article related to 'text mining'. Note that filters may be applied to display a sub-set of articles in this category (see FAQs on filtering for usage tips). Select this link to remove all filters.
Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally 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 text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). (Excerpt from Wikipedia article: Text mining)
See our 'text mining' overview for more data and comparisons with other tags. For visualisations of metadata related to timelines, bands of recency, top authors, and and overall distribution of authors using this term, see our 'text mining' usage charts.
Ariadne contributors most frequently referring to 'text mining':