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In this paper we investigate the efficiency of the novel term weighting algorithm for opinion mining and topic categorization of articles from newspapers and Internet. We compare the novel term weighting technique with existing approaches such as TF-IDF and ConfWeight. The performance on the data from the text-mining campaigns DEFT'07 and DEFT'08 shows that the proposed method can compete with existing information retrieval models in classification quality and that it is computationally faster. The proposed text preprocessing method can be applied in large-scale information retrieval and data mining problems and it can be easily transported to different domains and different languages since it does not require any domain-related or linguistic information.

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