Full Text:   <1894>

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CLC number: TP391.1

On-line Access: 2018-04-09

Received: 2016-11-02

Revision Accepted: 2017-03-09

Crosschecked: 2018-02-15

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Citations:  Bibtex RefMan EndNote GB/T7714


Xing Su


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.2 P.260-272


Words alignment based on association rules for cross-domain sentiment classification

Author(s):  Xi-bin Jia, Ya Jin, Ning Li, Xing Su, Barry Cardiff, Bir Bhanu

Affiliation(s):  Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; more

Corresponding email(s):   jiaxibin@bjut.edu.cn, jinya@emails.bjut.edu.cn, xingsu@bjut.edu.cn

Key Words:  Sentiment classification, Cross-domain, Association rules

Xi-bin Jia, Ya Jin, Ning Li, Xing Su, Barry Cardiff, Bir Bhanu. Words alignment based on association rules for cross-domain sentiment classification[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(2): 260-272.

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T1 - Words alignment based on association rules for cross-domain sentiment classification
A1 - Xi-bin Jia
A1 - Ya Jin
A1 - Ning Li
A1 - Xing Su
A1 - Barry Cardiff
A1 - Bir Bhanu
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601679

Automatic classification of sentiment data (e.g., reviews, blogs) has many applications in enterprise user management systems, and can help us understand people&x2019;s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules (WAAR) for cross-domain sentiment classification, which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon® datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.




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