Sentiment analysis – also known as opinion mining – gained a lot of popularity over the last decade. Prior literary research utilizing sentiment analysis focused on assessing the contents of entire novels for trends over the course of the narrative. Almost no research existed regarding the summaries of these stories. This paper sought to remedy that gap by applying sentiment analysis to Harry Potter Fanfiction story summaries for possible trends with the end goal of publishers and authors alike finding future use for these types of analysis. Four primary questions were posed to accomplish this. First, was there an overall positive or negative valence (total sum) of the dataset? Second, did that trend persist regardless of the sentiment analysis lexicon utilized? Third, would taking a subset of the data by a single rating change that trend? Finally, would a subset created on the top genres have any change on that trend? Overwhelmingly, the answer was a negative trend that only deviated slightly regardless of which lexicon was run or how the data was subdivided.