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Text preprocessing is an essential step in text mining. Removing words thatcan negatively impact the quality of prediction algorithms or are notinformative enough is a crucial storage-saving technique in text indexing andresults in improved computational efficiency. Typically, a generic stop wordlist is applied to a dataset regardless of the domain. However, many commonwords are different from one domain to another but have no significance withina particular domain. Eliminating domain-specific common words in a corpusreduces the dimensionality of the feature space, and improves the performanceof text mining tasks. In this paper, we present a novel mathematical approachfor the automatic extraction of domain-specific words called thehyperplane-based approach. This new approach depends on the notion of lowdimensional representation of the word in vector space and its distance fromhyperplane. The hyperplane-based approach can significantly reduce textdimensionality by eliminating irrelevant features. We compare thehyperplane-based approach with other feature selection methods, namely \c{hi}2and mutual information. An experimental study is performed on three differentdatasets and five classification algorithms, and measure the dimensionalityreduction and the increase in the classification performance. Results indicatethat the hyperplane-based approach can reduce the dimensionality of the corpusby 90% and outperforms mutual information. The computational time to identifythe domain-specific words is significantly lower than mutual information.

Accelerating Text Mining Using Domain-Specific Stop Word Lists