There is a lack of both data and, more significantly, computational analyses of the linguistic behavior near an individual's final moments of life. The present study is aimed to reduce human bias and save time in psycholinguistic studies by providing data-backed insights.
A novel machine learning based pipeline proposed, using elements of semantic similarity (BERT and transformers) and emotion extraction in collaboration, to analyze the final statements of death row inmates to understand the consistency in their verbal expression moments before their death. A new method of analysis was proposed in this study to explore the notions inherent in the statements. A large database of 466 final statements from death row inmates in Texas was utilized in this study. Manual notion analysis was validated by a computational method of notion inferencing.
Basic emotions of Anger and Fear majorly dominated the statements, constituting 54% of the whole, while 21% of all statements were of emotional states of Happiness and Serenity.
The outcomes of this study are expected to contribute to psychological analyses of humans, moments before death, and provide insights to criminology researchers to formulate better strategies of rehabilitation and debate the death penalty.