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        Study of Opinion Mining in Electronic Commerce Based on Imbalanced Data Classification and Part of Speech Analysis

        Wang Gang;Wang Jue;Yang Shanlin;School of Management,HeiFei University of Technology;The Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision;Center for Forecasting Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences;  
        With the popularization of electronic commerce,product reviews in the Internet are paid more and more attention when customers want to know the quality of products.Meanwhile,a lot of opinion mining techniques have been proposed to help customers to analyze these huge data.However,the imbalanced distribution of review datasets is paid less attention to.In this paper,a new method based on sentiment knowledge and machine learning is proposed.Firstly,two methods,i.e., "reserved POS method" and "left POS method",are used to analyze the POS of product reviews.Then,an new opinion mining method is proposed based on imbalanced data classification.Lastly,experiments using Ctrip dataset,JD dataset,and DangDang dataset,are conducted to verify the effectiveness of the proposed method.Experimental results reveal that the new method based on imbalanced data classification and POS analysis is effective to the opinion mining.And the best result was gotten when using Random Subspace and "left POS method".
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