Concise semantic analysis based text categorization using modified hybrid union feature selection approach

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Text categorization mainly comprises of deriving a representation of the corpus in a standard bag-of-words format. The merit of bag-of-word representations is that they considering every term as a feature, while the downside of this is that the computation cost increases with the number of features and the representation of relations between documents and features. Semantic analysis can help in gaining an edge through document and term correlation in a concept space. However, most semantic analysis techniques have their own limitations when used for text categorization. In this work, a Concise Semantic Analysis (CSA) technique that extracts concepts from corpus and then interpret the document & word relationship in a given concept space is proposed. To improve the performance of CSA, a novel feature selection technique called the Modified hybrid union (MHU) was designed, which considerably reduced computation time and cost. To experimentally validate the proposed approach, MHU based CSA was applied to the problem of text categorization. Experiments performed on standard data sets like Reuters-21578 and WSDL-TC, show that the proposed CSA with MHU approach significantly improved performance in terms of execution time and categorization accuracy.