Un usage du Text Mining : Donner du sens a la connaissance client

Manu CARRICANO, Grégoire DE LASSENCE

Abstract


Data Mining technologies have enhanced management research’s predictive capability. In recent years, many improvements have been made, among others by incorporating non-structured data to traditional models. This is an important challenge as non-structured data accounts for more than 80% of an organization’s knowledge. Text Mining allows researchers to use this type of data to optimize decision making processes. The goal of this paper is to describe Text Mining implementation and its contribution to management, in other words, the way non-structured data’s integration to traditional Data Mining models can optimize the predictive outcome of such analysis. The added-value of Text Mining is demonstrated as follows : first we show that Text Mining allows considerable enrichment of traditional data mining models through identification and analysis of the most relevant textual data ; second, through showing that the model with textual data over performs other models with structured data only. We analyze a case in the automotive industry that illustrates how a manufacturer can anticipate vehicles recall by combining structured and non-structured data, and avoid consequently the risk for its brand due to a bad crisis management.

Keywords


CRM;Data Mining;Text Mining;Analyse de Données Textuelles;gestion de la connaissance;knowledge management

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DOI: http://dx.doi.org/10.9876/sim.v14i2.240

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