Authors:
Brindha.G.R,Santhi.B,DOI NO:
https://doi.org/10.26782/jmcms.2018.10.00014Keywords:
Text processing,Feature weighting, Transductive Support Vector Machine,Cross domain classification,Abstract
Intra domain supervised classification of online reviews is vastly analysed by current studies. At the same time, the level of performance declines when training is performed with one domain and testing with reviews of a different domain. The main fact behind this reduction is the domain distribution difference and the feature vector difference. Also the semantic of each word in a corpus differs based on its usage in domains. The objective of this study is to propose a new similarity based feature weighting technique for text reviews for enhancing the accuracy of inter domain classification. Different training and testing domains are weighted by proposed probability based statistical techniques for the classification by Support Vector Machine (SVM) and Transductive Support Vector Machine (TSVM). TSVM performs much better for this cross domain classification. The fact behind the performance of TSVM is its Transductive learning even with the small training set. The correlation between source and target domain and its influence on classification accuracy are analysed in detail using the outcome of existing feature weighting and proposed weighting techniques.Refference:
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Author(s): Brindha. G.R, Santhi. B, View Download