Tion for Sentimental Analysis
نویسندگان
چکیده
Domain adaptation methods aims to improve the accuracy of the target predictive classifier while using the patterns from a related source domain that has large number of labeled data. To solve the domain adaptation problem, we propose new simple and intuitive method that can improve the learning of target data by calculating the distance of each instances in source and target domain. We added distance kernel based cross entropy term in loss function of logistic regression sentimental analysis classifier. We evaluated the proposed method by using cross domain sentiment analysis tasks of Amazon reviews in four domains. Our empirical results showed improvements in all 12 domain adaptation experiments.
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