  Sentiment analysis aims to uncover emotions conveyed through information. In
its simplest form, it is performed on a polarity basis, where the goal is to
classify information with positive or negative emotion. Recent research has
explored more nuanced ways to capture emotions that go beyond polarity. For
these methods to work, they require a critical resource: a lexicon that is
appropriate for the task at hand, in terms of the range of emotions it captures
diversity. In the past, sentiment analysis lexicons have been created by
experts, such as linguists and behavioural scientists, with strict rules.
Lexicon evaluation was also performed by experts or gold standards. In our
paper, we propose a crowdsourcing method for lexicon acquisition, which is
scalable, cost-effective, and doesn't require experts or gold standards. We
also compare crowd and expert evaluations of the lexicon, to assess the overall
lexicon quality, and the evaluation capabilities of the crowd.
