Research of cross－language sentiment classification based on structural correspondence learning
Wang Peng1，2，Zhao Xueli1，2，Li Nana1，2，Dong Yongfeng1，2*
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1.School of Computer Science & Engineering，Hebei University of Technology，Tianjin，300401，China； 2．Hebei Province Key Laboratory of Big Data Calculation(Hebei University of Technology)，Tianjin，300401，China
The main purpose of sentiment classification is to predict the sentiment polarity(positive or negative)of the data that the user publishes the emotional data on the Internet.The sentiment analysis of various languages has become a hotspot of many applications，but because of the different quality of sentiment resources and quantity on the imbalance，the researchers usually use the source language to improve the target language cross－language sentiment classification method to improve the accuracy of the target language sentiment classification.The traditional cross－language sentiment classification mainly maps the target language to the source language through machine translation，but the accuracy of the classification is seriously influenced by the quality of the machine translation.This paper discusses the structure of the cross－language(CLSCL)，and then put forward the M－CLSCL algorithm，by using the selected axis of the word to the target language of the language of the target language sentiment classification.Comparing the experimental results of M－CLSCL method and the relevant methods，we find that M－CLSCL has improved the accuracy of emotional classification.
Wang Peng1，2，Zhao Xueli1，2，Li Nana1，2，Dong Yongfeng1，2*.
Research of cross－language sentiment classification based on structural correspondence learning[J]. Journal of Nanjing University(Natural Sciences), 2017, 53(6): 1133
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