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In connection to literature, negative empathy is a sophisticated form of narrative empathy with fictional characters portrayed as markedly evil and seductive at the same time. Several studies on narrative engagement have explored negative empathy mainly from a theoretical perspective. Conversely, empirical approaches have rarely delved into the dynamics of the linguistic construction of the texts studied. To fill this gap, this paper employs computational techniques to investigate the language of a corpus of novels whose characters are particularly apt for the arousal of negative empathy. More specifically, this study uses Sentiment and Emotion Analysis to explore the lexical representation of emotions and to locate fluctuations in the emotional content of the texts. The ultimate aim is to assess both the potential and the vulnerabilities of Sentiment Analysis for detecting emotional shifts in a literary text and thus for revealing the intensity of its emotional content, which may facilitate the readers’ morally challenging engagement with negative characters.

Exploring the potential of sentiment analysis for the study of negative empathy