There are several areas of application for emotion detection systems, for example social media analysis, for which it is important to reliably recognize expressed emotions. This thesis takes negations, intensifiers and diminishers on emotion expressions in Tweets into account, in order to study whether this can improve an emotion detection system. It uses different emotion classifiers together with various modifier detection approaches to evaluate the impact of modifiers on emotion expressions. The results show that an emotion detection system can be slightly improved if negations are taken into account. The thesis also studies the correlation between modified emotion words and basic emotions to obtain a better understanding about modified emotions. The analysis of the results shows correlations between modified and basic emotions, which enables us to determine the expressed basic emotion of modified emotion words.