Sentiment analysis is the method of computationally recognising opinions stated in a piece of text, particularly to identify if the writer has a positive, negative, or neutral attitude towards a...Show moreSentiment analysis is the method of computationally recognising opinions stated in a piece of text, particularly to identify if the writer has a positive, negative, or neutral attitude towards a given topic. Although sentiment analysis is commonly used to analyse short texts on social media platforms, its application in literary research has gained traction in recent years. The emergence of sentiment analysis tools, such as Syuzhet, has notably expanded the research possibilities in this field. However, despite these advancements, there remains a need to further explore and understand the strengths and limitations of sentiment analysis in the context of literary analysis. The aim of this thesis is to further the body of knowledge about the use of sentiment analysis as a technique for plot extraction. Throughout this thesis, I experiment with a modified form of the social media analysis tool VADER. While this method proofs to work as an effective tool for extracting sentiment from linear stories, it still shows limitation on a sentence-to-sentence basis. Moreover, I use this tool to replicate an often-cited study by Reagan et al., where it was stated that the majority of stories can be categorized in six basic ‘plot shapes’. I argue that while most of these shapes can still be identified using an alternative sentiment analysis technique, this technique occasionally classifies a story into a different shape than Reagan et al.’s analysis did. I conclude by giving a critical evaluation of sentiment analysis as a tool for plot extraction. Since the ‘plot’ of a story is a multifaceted concept, we cannot simply argue that a sentiment analysis graph displays the progression of plot. Nonetheless, modifications can be made to get a fuller understanding of the narrative of a story.Show less