Beyond Plot: How Sentiment Analysis Reshapes Our Understanding of Narrative Structure
Bibliographic record
Abstract
Sentiment analysis, particularly with the advent of large language models, is reshaping our understanding of narrative. Emotional arcs in both fictional and non-fictional narratives surface latent structures that challenge traditional notions of plot and character. One reason is that feature extraction correlates with passages often selected for close reading, suggesting the role of emotion in passage selection has been undertheorized. Case studies, moreover, show that common critiques of sentiment analysis are misplaced, and the method can identify and contrast cultural differences, locate translation effects, and illuminate collective emotional experiences. Both the strengths and limitations of sentiment analysis in understanding affect and emotion in literature are detailed to help address misconceptions. The method, as it turns out, now offers a powerful tool for highlighting emotional structure in narrative, complementing traditional literary analysis and reshaping our understanding of what constitutes a story. Sentiment analysis for literary studies asks us to reconsider not just how we analyze individual texts, but how we conceptualize the very nature of narrative itself.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".