  Deriving prior polarity lexica for sentiment analysis - where positive or
negative scores are associated with words out of context - is a challenging
task. Usually, a trade-off between precision and coverage is hard to find, and
it depends on the methodology used to build the lexicon. Manually annotated
lexica provide a high precision but lack in coverage, whereas automatic
derivation from pre-existing knowledge guarantees high coverage at the cost of
a lower precision. Since the automatic derivation of prior polarities is less
time consuming than manual annotation, there has been a great bloom of these
approaches, in particular based on the SentiWordNet resource. In this paper, we
compare the most frequently used techniques based on SentiWordNet with newer
ones and blend them in a learning framework (a so called 'ensemble method'). By
taking advantage of manually built prior polarity lexica, our ensemble method
is better able to predict the prior value of unseen words and to outperform all
the other SentiWordNet approaches. Using this technique we have built
SentiWords, a prior polarity lexicon of approximately 155,000 words, that has
both a high precision and a high coverage. We finally show that in sentiment
analysis tasks, using our lexicon allows us to outperform both the single
metrics derived from SentiWordNet and popular manually annotated sentiment
lexica.
