An Approach to Cross-Lingual Sentiment Lexicon Construction

Abstract

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.