Learning Sentiment Lexicons in Spanish

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.