From words to paragraphs: modeling sentiment dynamics in ‘notes from underground’ with GPT-4 via descriptive methods and differential equations

Abstract

This study examines how the sentiment values in the first part of the book entitled as “Underground” of Fyodor Dostoevsky’s “Notes from Underground” change from words to sentences to paragraphs. Using the GPT-4 language model, we conducted a descriptive analysis of standardized sentiment values and calculated cumulative sentiment trajectories over the text. We then created differential equation models to model the sentiment tones using regression analysis. Our findings suggest that sentiment becomes less negative from words to paragraphs, indicating that context moderates negativity. Paragraph sentiment was also more stable with lower variability. There was a narrative arc of initial decline followed by an upward turn in sentiment. Paragraphs had the highest baseline sentiment, suggesting that they are able to capture more nuanced context. Paragraphs lost short-term sentiment quickly but retained longterm sentiment longest, aligning with paragraphs maintaining overall text sentiment over time. These findings suggest that there are complex dynamics between linguistic units contributing to perceived stability of sentiment. Quantitative decay rates are useful indicators but do not fully characterize sentiment stability.