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Study 2 - Predicting e-commerce company success by mining the text of its publicly-accessible website

: Thorleuchter, D.; Poel, D. van den

Thorleuchter, D. ; Poel, D. van den; Prinzie, A.:
Essays on text mining for improved decision making : Thesis contains seven Fraunhofer INT studies
Zelzate, Belgien: University press, 2011
Book Article
Fraunhofer INT ()
success measure; E-Commerce; SVD; classification; text mining; Website

We analyze the impact of textual information from e-commerce companies' web sites on their commercial success. The textual information is extracted from web content of e-commerce companies divided into the top 100 worldwide most successful companies and into the top 101 to 500 worldwide most successful companies. It is shown that latent semantic concepts extracted from the analysis of textual information can be adopted as success measures for a top 100 e-commerce company classification. This contributes to the existing literature concerning web site success measures for e-commerce. As evaluation, a regression model based on these concepts is built that is successful in predicting the commercial success of the top 100 companies. These findings are valuable for e-commerce web sites creation.