![]() ![]() Building on previous approaches using natural language processing to study the contents and structure of suicide notes (Teixeira et al., 2021), we described how emotions are recalled, organized, and distributed within such narratives. In this study, we used network science to uncover the emotional structure of suicide notes. This challenging task requires the adoption of interpretable algorithms (as opposed to “black box" models such as deep neutral networks) that afford a greater understanding of the mental patterns and ways of thinking expressed in the final notes of those who completed suicide. Despite recent progress in using Big Data and natural language processing for the automatic detection and discrimination of suicide notes from other types of digital texts (Schoene and Dethlefs, 2016 Bayram et al., 2022), more work is required to understand how people who completed suicide organize and express emotions in their final writings (Palmier-Claus et al., 2012 Hallensleben et al., 2019). It represents an extreme behavior that often arises from negative perceptions and distorted mindsets about the self, others and life itself (Rizk et al., 2019 Oquendo et al., 2020). Suicide is the outcome of complex, distressed emotional processing. Our results demonstrate that suicide notes possess highly structured and contrastive narratives of emotions, more complex than expected by null models and healthy populations. Both the groups of authors of suicide notes and healthy individuals exhibit less complexity than random expectation. An entropy measure identified a similar tendency for suicide notes to shift more frequently between contrasting emotional states. At the group level, authors of suicide narratives display a higher complexity than healthy individuals, i.e., lower levels of coherently valenced emotional states in triads. Supported by psychological literature, we introduce emotional complexity as an affective analog of structural balance theory, measuring how elementary cycles (closed triads) of emotion co-occurrences mix positive, negative and neutral states in narratives and recollections. Furthermore, by using data from the Emotional Recall Task, we model emotional transitions within these notes as co-occurrence networks and compare their structure against emotional recalls from mentally healthy individuals. Through emotional profiling, their ending statements are found to be markedly more emotional than their main body: The ending sentences in suicide notes elicit deeper fear/sadness but also stronger joy/trust and anticipation than the main body. We find that, despite their negative context, suicide notes are surprisingly positively valenced. Using network science and word co-occurrences, we reconstruct conceptual associations as communicated in 139 genuine suicide notes, i.e., notes left by individuals who took their lives. Communicating one's mindset means transmitting complex relationships between concepts and emotions. ![]()
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