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Text Mining in the Context of Business Intelligence

Text Mining in the Context of Business Intelligence
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Author(s): Hércules Antonio do Prado (Brazilian Enterprise for Agricultural Research and Catholic University of Brasilia, Brazil), José Palazzo Moreira de Oliveira (Federal University of Rio Grande do Sul, Brazil), Edilson Ferneda (Catholic University of Brasilia, Brazil), Leandro Krug Wives (Centro Universitário Feevale and Federal University of Rio Grande do Sul, Brazil), Edilberto Magalhães Silva (Brazilian Public News Agency, Brazil) and Stanley Loh (Catholic University of Pelotas and Lutheran University of Brazil, Brazil)
Copyright: 2005
Pages: 6
Source title: Encyclopedia of Information Science and Technology, First Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59140-553-5.ch496

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Abstract

Information about the external environment and organizational processes are among the most worthwhile input for business intelligence (BI). Nowadays, companies have plenty of information in structured or textual forms, either from external monitoring or from the corporative systems. In the last years, the structured part of this information stock has been massively explored by means of data-mining (DM) techniques (Wang, 2003), generating models that enable the analysts to gain insights on the solutions for organizational problems. On the text-mining (TM) side, the rhythm of new applications development did not go so fast. In an informal poll carried out in 2002 (Kdnuggets), just 4% of the knowledge-discovery-from-databases (KDD) practitioners were applying TM techniques. This fact is as intriguing as surprising if one considers that 80% of all information available in an organization comes in textual form (Tan, 1999).

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