期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2011
卷号：2011
出版社：ACL Anthology
摘要：Most previous work on multilingual sentiment
analysis has focused on methods to adapt
sentiment resources from resource-rich
languages to resource-poor languages. We
present a novel approach for joint bilingual
sentiment classification at the sentence level
that augments available labeled data in each
language with unlabeled parallel data. We rely
on the intuition that the sentiment labels for
parallel sentences should be similar and present
a model that jointly learns improved monolingual
sentiment classifiers for each language.
Experiments on multiple data sets show that the
proposed approach (1) outperforms the monolingual
baselines, significantly improving the
accuracy for both languages by 3.44%-8.12%;
(2) outperforms two standard approaches for
leveraging unlabeled data; and (3) produces
(albeit smaller) performance gains when
employing pseudo-parallel data from machine
translation engines.
