A key element for decision makers to track is their stakeholders' sentiment. Recent developments show a tendency of including various aspects other than word frequencies in automated sentiment analysis approaches. One of these aspects is negation, which can be accounted for in various ways. We compare several approaches to accounting for negation in sentiment analysis, differing in their methods of determining the scope of influence of a negation keyword. On a set of English movie review sentences, the best approach is to consider two words, following a negation keyword, to be negated by that keyword. This method yields a significant increase in overall sentiment classification accuracy and macro-level F1 of 5.5% and 6.2%, respectively, compared to not accounting for negation. Additionally optimizing sentiment modification of negated words to a value of -1.27 rather than -1 yields a significant 7.1% increase in accuracy and a significant 8.0% increase in macro-level F1.