Can the Key Signature, Mode, and the Beats per Minute of a Song Predict the Emotional Tone of Popular Music Lyrics?
Bibliographic record
Abstract
The Dictionary of Affect (Whissell, 2009) was used to analyze the lyrics in popular music to determine if the emotional tone of the lyrics (pleasantness, activation, and imagery) was associated with various components of the popular music (key signature, mode, and beats per minute).The first analysis (N = 80 songs; 21,176 words) examined the relationship between the key signature (for example, the keys of A, B, C, D, E, F, or G; disregarding the major or minor mode) and the emotional tone of the lyrics, but no significant associations were noted.In the second investigation (N = 60 songs; 17,281 words), the song's mode (major or minor key signatures) was related to significant differences in the lyrics' pleasantness (p = .028).Song lyrics written in major modes (M = 55.5) were significantly more pleasant than those written in minor modes (M = 52.8);however, the mean pleasantness of the minor song lyrics was still in the pleasant range for the Dictionary of Affect.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".