Knowledge, being context-specific and bound to individuals, is strongly related to human emotions such as joy or fear. Although emotions play an important role to articulate knowledge in text, KM research only offers insight on emotions from specific angles, neglecting a holistic view. Applying a sentiment analysis, this study closes the aforementioned gap by investigating the occurrence of emotions in KM publications. Based on general sentiment dictionaries, we (1) develop a dictionary aligned with KM, and (2) apply it to KM publications to determine the presence of positive and negative emotions and categorize them according to an emotion scale. Our results reveal that a variety of emotions is expressed in KM studies, both positive and negative, proving its relevance for this domain. We find that there is high term diversity, but also the need for consolidation of terms as well as emotion categories in KM.