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text mining

Process of analysing text to extract information from it

EntityQ676880

Also known as text data mining · textmining · text-mining · text analytics · text analysis · automatic text analysis

Subclass of: text and data mining
1
Sources
1.id.ndl.go.jp/auth/ndlsh/01119322
View statement on Wikidata ↗
, natural language processing
1
Sources
1.id.ndl.go.jp/auth/ndlsh/01119322
View statement on Wikidata ↗

Overview

From Wikipedia, the free encyclopedia · Read full article · Text CC BY-SA 4.0

Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005), there are three perspectives of text mining: information extraction, data mining, and knowledge discovery in databases (KDD). Text mining usually involves the process of structuring the input text, 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 interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.

Overview

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30 statements · 33% with external sources · numbers in brackets are citations

Has use: science
digital humanities
Different from: text analysis

Data quality

statements

33%

externally sourced

Source last edited August 31, 2026. AIDB refreshes this page daily.

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AIDB. (2026). text mining (Q676880). Retrieved from https://aidb.si/e/text-mining-Q676880