Language or rating scales based classifications of emotions: computational analysis of language and alexithymia
Bibliographic record
Abstract
Rating scales are the dominating tool for the quantitative assessment of mental health. They are often believed to have a higher validity than language-based responses, which are the natural way of communicating mental states. Furthermore, it is unclear how difficulties articulating emotions-alexithymia-affect the accuracy of language-based communication of emotions. We investigated whether narratives describing emotional states are more accurately classified by questions-based computational analysis of language (QCLA) compared to commonly used rating scales. Additionally, we examined how this is affected by alexithymia. In Phase 1, participants (N = 348) generated narratives describing events related to depression, anxiety, satisfaction, and harmony. In Phase 2, another set of participants summarized the emotions described in the narratives of Phase 1 in five descriptive words and rating scales (PHQ-9, GAD-7, SWLS, and HILS). The words were quantified with a natural language processing model (i.e., LSA) and classified with machine learning (i.e., multinomial regression). The results showed that the language-based responses can be more accurate in classifying the emotional states compared to the rating scales. The degree of alexithymia did not influence the correctness of classification based on words or rating scales, suggesting that QCLA is not sensitive to alexithymia. However, narratives generated by people with high alexithymia were more difficult to classify than those generated by people with low alexithymia. These results suggest that the assessment of mental health may be improved by language-based responses analyzed by computational methods compared to currently used rating scales.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".