Computational Modeling of Subjectivity in First-Person Narratives for Identifying Diegetic and Extradiegetic Private States
Kenji Sagae, Andrew S. Gordon, Morteza Dehghani, Mike Metke, Jackie S. Kim, Sarah I. Gimbel, Christine Tipper, Jonas Kaplan, Mary Helen Immordino-Yang
Journal of Data Processing, Vol. 15, No. 3, 2025.
DOI: https://doi.org/10.6025/jdp/2025/15/3/124-140
Link: https://www.dline.info/jdp/fulltext/v15n3/jdpv15n3_3.pdf
Abstract:
The paper explores the identification of subjective language in personal narratives, focusing on distinguishing
between two narrative levels: diegetic (events within the story) and extradiegetic (the narratorâ€™s reflections).
Subjective language, expressing emotions, opinions, and mental states, plays a crucial role in shaping
the audienceâ€™s interpretation. The study uses a dataset of 40 annotated personal weblog narratives, employing
text classification techniques to automatically identify subjectivity at both levels. A multiclass classification
model is trained using features like bag-of-words and part-of-speech tags. Results show a 58% accuracy
for six-way classification, outperforming a baseline. Binary classifications for subjectivity and narrative
level achieve 78% and 81% accuracy, respectively. Despite limitations due to a small dataset, the findings
highlight the feasibility of computational modeling for analyzing narrative subjectivity, with potential applications
in sentiment analysis, information retrieval, and commonsense knowledge extraction.