Learning Sentiment Lexicons in Spanish

Abstract

In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English. We show that bridging the language gap using the multilingual sense-level aligned WordNet structure allows us to generate a high accuracy (90\%) polarity lexicon comprising 1,347 entries, and a disjoint lower accuracy (74\%) one encompassing 2,496 words. By using an LSA-based vectorial expansion for the generated lexicons, we are able to obtain an average F-measure of 66\% in the target language. This implies that the lexicons could be used to bootstrap higher-coverage lexicons using in-language resources.

Tasks

Reproductions