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《Engineering Journal of Wuhan University》 2017-02
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Cross-sampling and structural sentiment fusion based cross-lingual sentiment analysis

CHEN Qiang;HE Yanxiang;LIU Xule;LIU Jianbo;School of Computer Science,Wuhan University;State Key Lab of Software Engineering,Wuhan University;  
Based on co-training,we propose a mutual-learning framework for cross-lingual sentiment analysis based on a cross-sampling strategy and the structural sentimental information.Firstly,we use a heuristic method to extract sentimental expressions from training data;and then we join them into n-gram features to form a highly sentiment-expressive feature space.Subsequently,we integrate into traditional cotraining framework with a cross-sampling strategy to mutually learn the sentimental knowledge from unlabeled data in the both two languages.During the learning,sentimental knowledge from different languages are mutually fused to each other language.Finally,we can learn a sentiment classifier in the source language with our proposed framework.The experimental results show that our proposed method can efficiently leverage a small scale of a labeled data and massive unlabeled data in the both languages to get a more dependable and high-quality sentiment classifier in the target language comparing to existing crosslingual sentiment analysis(CLSA)methods.
【Fund】： 国家自然科学基金项目(编号:61472290 61472291)
【CateGory Index】： TP391.1
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