Operationalizing Narrativized Change in the Political Talk for Computational Recognition

This article furthers the methodology for computational recognition of narratives in argumentative language use.Narratives are understood as a cognitive and rhetorical tool for making sense of change and the unexpected, as well as arguing for a point.Building on narrative theory and linguistic knowledge, this study operationalizes narrative as the linguistic portrayal of experienced change.Our data consists of Finnish parliamentary records .Agentive experientiality plays a vital role in political speech, where deliberation over different choices and outcomes takes place.Our methodology relies on identifying verbs that encode cognitive and emotional shifts -key signals of narrative experientiality -based on a tailored semantic resource.Using Deptreepy, a search tool based on dependency trees, these verb classes were systematically extracted from a pre-existing sample of 60 manually annotated plenary session transcripts, where the annotation marked narrative and non-narrative segments.This approach offers a method for identifying narratives in complex, rhetorically layered genres that is compatible with low-resource languages.Results show that particular semantic verb classesespecially those indicating mental and emotional change -serve as effective indicators of narrativity.The study contributes to both narrative theory and computational linguistics by demonstrating how semantic classification of verbs, rooted in linguistic and narratological theory, can yield a viable tool for extracting narratives in argumentative language use.It also highlights how experientiality is not only conveyed in the stories told but also embedded in the situation of the telling, often amplified through cognitive stance verbs that address the audience's shared knowledge or memories.These findings suggest a dual layer of experiential engagement in parliamentary narratives, reinforcing their argumentative power.