@article{4547,
  author = {Kenji Sagae, Andrew S. Gordon, Morteza Dehghani, Mike Metke, Jackie S. Kim, Sarah I. Gimbel, Christine Tipper, Jonas Kaplan, Mary Helen Immordino-Yang},
  title = {Computational Modeling of Subjectivity in First-Person Narratives for Identifying Diegetic and Extradiegetic Private States},
  journal = {Journal of Data Processing},
  year = {2025},
  volume = {15},
  number = {3},
  doi = {https://doi.org/10.6025/jdp/2025/15/3/124-140},
  url = {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 narrator’s reflections).
Subjective language, expressing emotions, opinions, and mental states, plays a crucial role in shaping
the audience’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.},
}