An Unsupervised Data-Driven Cross-Lingual Method for Building High Precision Sentiment Lexicons

In this paper we present a completely unsupervised approach for creating a sentiment lexicon. The approach has been realized by designing a pipeline which implements an unsupervised system that covers different aspects: the automatic extraction of user reviews, the pre-processing of text, the use of a scoring measure which combines: entropy, term frequency, inverse document frequency, and finally a cross lingual intersection. We have validated the approach though the analysis of a previews present in the Google Play market. The results show the effectiveness of the approach given by satisfactory values of precision for the obtained lexicon.