Using sentiment analysis to detect affect in children’s and adolescents’ poetry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sentiment analysis is a computational method that automatically analyzes the valence of massive quantities of text. Basic sentiment analysis involves extracting and counting emotionally-laden keywords from passages of text (e.g., hate, love, happy, sad). This study describes using sentiment analysis to explore changes in emotion expression in a developmental context. A sample of n = 8,688 poems published online by children and adolescents from Grade 4 to Grade 12 was analyzed. Sentiment analysis coded words as positive or negative and these were averaged within each poem to obtain its relative percentage of positive and negative sentiment. Polynomial regressions explored linear and nonlinear trends in sentiment scores by grade. Among the results, negative sentiment demonstrated an upward curvilinear trend, increasing sharply from Grade 6 to Grade 11 and then decreasing afterward. Positive sentiment demonstrated a sinusoidal pattern throughout development. Overall, these findings are consistent with previous research on the progressions of emotion expression in childhood and adolescence. Despite some limitations, sentiment analysis presents an opportunity for researchers in developmental psychology to explore basic questions in emotional development using large quantities of data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it