Semi-supervised Acquisition of Croatian Sentiment Lexicon

Sentiment analysis aims to recognize subjectivity expressed in natural language texts. Subjectivity analysis tries to answer if the text unit is subjective or objective, while polarity analysis determines whether a subjective text is positive or negative. Sentiment of sentences and documents is often determined using some sort of a sentiment lexicon. In this paper we present three different semi-supervised methods for automated acquisition of a sentiment lexicon that do not depend on pre-existing language resources: latent semantic analysis, graph-based propagation, and topic modelling. Methods are language independent and corpus-based, hence especially suitable for languages for which resources are very scarce. We use the presented methods to acquire sentiment lexicon for Croatian language. The performance of the methods was evaluated on the task of determining both subjectivity and polarity at (subjectivity + polarity task) and the task of determining polarity of subjective words (polarity only task). The results indicate that the methods are especially suitable for the polarity only task.