Personal narratives are stories authors construct to make meaning of their experiences.Style, the distinctive way authors use language to express themselves, is fundamental to how these narratives convey subjective experiences.Yet there is a lack of a formal framework for systematically analyzing these stylistic choices.We present a novel approach that formalizes style in personal narratives as patterns in the linguistic choices authors make when communicating subjective experiences.Our framework integrates three domains: functional linguistics establishes language as a system of meaningful choices, computer science provides methods for automatically extracting and analyzing sequential patterns, and these patterns are linked to psychological observations.Using language models, we automatically extract linguistic features such as processes, participants, and circumstances.We apply our framework to hundreds of dream narratives, including a case study on a war veteran with post-traumatic stress disorder.Analysis of his narratives uncovers distinctive patterns, particularly how verbal processes dominate over mental ones, illustrating the relationship between linguistic choices and psychological states.

The study of dreams has been central to understanding human (un)consciousness, cognition, and culture for centuries. Analyzing dreams quantitatively depends on labor-intensive, manual annotation of dream narratives. We automate this process through a natural language sequence-to-sequence generation framework. This paper presents the first study on character and emotion detection in the English portion of the open DreamBank corpus of dream narratives. Our results show that language models can effectively address this complex task. To get insight into prediction performance, we evaluate the impact of model size, prediction order of characters, and the consideration of proper names and character traits. We compare our approach with a large language model using in-context learning. Our supervised models perform better while having 28 times fewer parameters. Our model and its generated annotations are made publicly available.