more and more urgent as virtual utterances of opinions or sentiment are becoming increasingly abundant on the Web. The role of negation in sentiment analysis has been explored only to a limited
extent. In this paper, we investigate the impact of accounting for negation in sentiment analysis. To this end, we utilize a basic sentiment analysis framework – consisting of a wordbank creation part
and a document scoring part – taking into account negation. Our experimental results show that by accounting for negation, precision relative to human ratings increases with 1.17%. On a subset of
selected documents containing negated words, precision increases with 2.23%