A Comparison of Two Lists Providing Emotional Norms for English Words (ANEW and the DAL)
Bibliographic record
Abstract
Although different in terms of purpose, word-selection procedures, and rating scales, both the ANEW (n = 1034) and DAL (n = 8742) lists, which have 633 words in common, provide normative emotional ratings for English words. This research compared the lists and cross-validated the two main lexical dimensions of affect. Parallel representatives of the two dimensions (Valence and Pleasantness, Arousal and Activation) were correlated across lists (rs = .86, .63). In tune with their separate purposes, the ANEW list, which was designed to describe emotional words, included more rare words, while the DAL, which was designed for natural language applications, included more common ones. The Valence-Activation scatterplot for ANEW was C-shaped and included fewer Arousing words of medium Valence, such as "awake," "debate," and "proves," while the DAL included fewer less common words descriptive of emotion such as "maniac," "corrupt," and "lavish." In view of these differences, list similarities strongly support the generalizability of the two main lexical dimensions 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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".