TY - GEN
T1 - Semi-supervised learning on cross-lingual sentiment analysis with space transfer
AU - He, Xiaonan
AU - Zhang, Hui
AU - Chao, Wenhan
AU - Wang, Deqing
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/10
Y1 - 2015/8/10
N2 - In the task of cross-language sentiment classification, the monolingual machine learning based approaches suffer from the shortage of available sentiment resources in target language. In order to reduce the cost of labeling the documents from a new language, many proposed approaches transfer the sentiment knowledge from resource-rich languages (e.g. English) to resource-poor languages (e.g. Chinese). Although the labeled data are only available in source language, the utilization of the sentiment information in target language is often disregarded. In this paper, we propose a semi-supervise learning approach with space transfer to tackle the above task. The main idea of our method is trying to take advantage of the intrinsic sentiment knowledge in target language and to replenish the lost information during the transfer process. The empirical results demonstrate that our method outperforms the state-of-the-art without using any parallel corpora.
AB - In the task of cross-language sentiment classification, the monolingual machine learning based approaches suffer from the shortage of available sentiment resources in target language. In order to reduce the cost of labeling the documents from a new language, many proposed approaches transfer the sentiment knowledge from resource-rich languages (e.g. English) to resource-poor languages (e.g. Chinese). Although the labeled data are only available in source language, the utilization of the sentiment information in target language is often disregarded. In this paper, we propose a semi-supervise learning approach with space transfer to tackle the above task. The main idea of our method is trying to take advantage of the intrinsic sentiment knowledge in target language and to replenish the lost information during the transfer process. The empirical results demonstrate that our method outperforms the state-of-the-art without using any parallel corpora.
KW - Cross-language Sentiment Analysis
KW - Semi-supervised learning
KW - Transfer Learning
UR - https://www.scopus.com/pages/publications/84959566751
U2 - 10.1109/BigDataService.2015.57
DO - 10.1109/BigDataService.2015.57
M3 - 会议稿件
AN - SCOPUS:84959566751
T3 - Proceedings - 2015 IEEE 1st International Conference on Big Data Computing Service and Applications, BigDataService 2015
SP - 371
EP - 377
BT - Proceedings - 2015 IEEE 1st International Conference on Big Data Computing Service and Applications, BigDataService 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2015
Y2 - 30 March 2015 through 3 April 2015
ER -