As of June 17, 2019, Wikipedia defines text mining as "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 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 (i.e., learning relations between named entities)."
Text mining may generally be differentiated from qualitative data analysis of texts, which uses software such as NVivo (which is licensed by AU), QDA Miner, or Atlas.ti, in that qualitative data analysis focuses on content that the researcher already knows or has "consumed" (by having read/viewed/heard it), and now categorizes, reviews, and/or classifies the content, whereas the number and/or volume of texts involved in text mining would make that impossible and therefore requires computerized statistical and natural language processing approaches. (That is not a clearly defined boundary, however.)
There are essentially two approaches to text mining:
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