Bootstrapping large scale polarity lexicons through advanced distributional methods

Recent interests in Sentiment Analysis brought the attention on effective methods to detect opinions and sentiments in texts. Many approaches in literature are based on hand-coded resources that model the prior polarity of words or multi-word expressions. The development of such resources is expensive and language dependent so that they cannot fully cover linguistic sentiment phenomena. This paper presents an automatic method for deriving large-scale polarity lexicons based on Distributional Models of Lexical Semantics. Given a set of heuristically annotated sentences from Twitter, we transfer the sentiment information from sentences to words. The approach is mostly unsupervised, and experiments on different Sentiment Analysis tasks in English and Italian show the benefits of the generated resources