In this paper, the authors investigate the topic-independent Chinese sentiment identification problem from online news. They analyze the word usage and sentence structure of the documents for inferring representative terms and sentences in the documents, and then employ the feature values of...

Dimensionality reduction of feature vector size plays a vital role in enhancing the text processing capabilities; it aims in reducing the size of the feature vector used in the mining tasks (classification, clustering, etc.). This paper proposes an efficient approach to be used in reducing the...

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of...

Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of extracting a linear structure from a high dimensional data based on estimating a low-dimensional non-Gaussian data component. In this paper we discuss a new approach to direct estimation of the projector on the target...

Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of extracting a linear structure from a high dimensional data based on estimating a low-dimensional non-Gaussian data component. In this paper we discuss a new approach to direct estimation of the projector on the target...