A Data-Driven Approach for Classification of Subjectivity in Personal Narratives

Abstract

Personal narratives typically involve a narrator who participates in a sequence of events in the past. The narrator is therefore present at two narrative levels: (1) the extradiegetic level, where the act of narration takes place, with the narrator addressing an audience directly; and (2) the diegetic level, where the events in the story take place, with the narrator as a participant (usually the protagonist). Although story understanding is commonly associated with semantics of the diegetic level (i.e., understanding the events that take place within the story), personal narratives may also contain important information at the extradiegetic level that frames the narrated events and is crucial for capturing the narrator’s intent. We present a data-driven modeling approach that learns to identify subjective passages that express mental and emotional states of the narrator, placing them at either the diegetic or extradiegetic level. We describe an experiment where we used narratives from personal weblog posts to measure the effectiveness of our approach across various topics in this narrative genre.