An Approach to Cross-lingual Sentiment Lexicon Construction

被引：5
|
作者：
Chang, Chia-Hsuan [1 ]
Wu, Ming-Lun [1 ]
Hwang, San-Yih [1 ]
机构：
[1] Natl Sun Yat Sen Univ, Dept Informat Management, Kaohsiung, Taiwan
来源：
2019 IEEE INTERNATIONAL CONGRESS ON BIG DATA (IEEE BIGDATA CONGRESS 2019) | 2019年
关键词：
text mining; sentiment analysis; cross-lingual lexicon learning; skip-gram; lexical relation;
D O I：
10.1109/BigDataCongress.2019.00030
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Lexicon-based sentiment analysis is a popular and practical approach for sentiment analysis. However, sentiment lexicons, which may be abundant in some language such as English, are scarce in many other languages. The cross-lingual lexicon learning aims to extend lexicons for the language with less resources from those lexicons available in other languages. In this paper, we propose an approach that builds a skip-gram variant to map word spaces across languages so as to construct lexicons for the language with less resources. We show in our preliminary experiment that our approach can generate lexicons that are similar to those crafted by human experts.
引用
收藏
页码：129 / 131
页数：3
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