A Comparative Study of Cross-Lingual Sentiment Classification

The task of sentiment classification relies heavily on sentiment resources, including annotated lexicons and corpus. However, the sentiment resources in different languages are imbalanced. In particular, many reliable English resources are available on the Web, while reliable Chinese resources are s...

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Bibliographic Details
Published in:2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Vol. 1; pp. 24 - 31
Main Author: Wan, Xiaojun
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2012
Subjects:
comparative study
cross-lingual sentiment classification
Lexicon
Machine translation
Reliability
Reviews
Semantics
Sentiment analysis
Support vector machines
Text categorization
Training
Translation
ISBN:9781467360579, 1467360570
Online Access:Get full text
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Summary:The task of sentiment classification relies heavily on sentiment resources, including annotated lexicons and corpus. However, the sentiment resources in different languages are imbalanced. In particular, many reliable English resources are available on the Web, while reliable Chinese resources are scarce till now. Cross-lingual sentiment classification is a promising way for addressing the above problem by leveraging only English resources for Chinese sentiment classification. In this study, we conduct a comparative study to explore the challenges of cross-lingual sentiment classification. Different schemes for cross-lingual sentiment classification based on two dimensions have been compared empirically. Lastly, we propose to combine the different individual schemes into an ensemble. Experiment results demonstrate the effectiveness of the proposed method.
ISBN:9781467360579
1467360570
DOI:10.1109/WI-IAT.2012.54