JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2018, Vol. 53 ›› Issue (3): 13-23.doi: 10.6040/j.issn.1671-9352.0.2017.064

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Deep representative learning based sentiment analysis in the cross-lingual environment

YU Chuan-ming1, FENG Bo-lin1, TIAN Xin1, AN Lu2*   

  1. 1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, Hubei, China;
    2. School of Information Management, Wuhan University, Wuhan 430072, Hubei, China
  • Received:2017-04-15 Online:2018-03-20 Published:2018-03-13

Abstract: Transfer learning focuses on solving the problem that it is difficult for supervised learning to obtain good classification results with the small training sets. Compared with the traditional supervised methods, it does not require the training and testing sets follow the same or similar data distributions. The model can be trained on the language which has rich data and labeling resources(source language), and the source and target language documents can be projected into the same feature space. In this way the large data from the source domain or language can be leveraged to solve the low performance problem in the target language. The reviews of three product categories, i.e. books, DVD and music, from Amazon, which are written in Chinese, English and Japanese, are collected as the experimental data. A novel model, i.e. the Cross Lingual Deep Representation Learning(CLDRL)is proposed and empirical study is conducted upon the experimental data. From the experimental results, it shows that the best performance of CLDRL achieves 78.59%, which prove the effectiveness of the proposed model.

Key words: cross language, deep representation learning, transfer learning, sentiment analysis

CLC Number: 

  • TP391
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