Accounting for negation in sentiment analysis

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%