  This paper presents a simple, robust and (almost) unsupervised
dictionary-based method, qwn-ppv (Q-WordNet as Personalized PageRanking Vector)
to automatically generate polarity lexicons. We show that qwn-ppv outperforms
other automatically generated lexicons for the four extrinsic evaluations
presented here. It also shows very competitive and robust results with respect
to manually annotated ones. Results suggest that no single lexicon is best for
every task and dataset and that the intrinsic evaluation of polarity lexicons
is not a good performance indicator on a Sentiment Analysis task. The qwn-ppv
method allows to easily create quality polarity lexicons whenever no
domain-based annotated corpora are available for a given language.
