Mining affective words to capture customer’s affective response to apparel products
Bibliographic record
Abstract
Contemporary apparel design practice is such that the functional and ergonomic aspects can be readily rationalized and thus computerized, but this is not true for the aspects of affect or emotion. Design for emotion or affect remains an ad hoc exercise. In the apparel industry, affects or emotions of both wearers and audiences are very important. This paper presents a work with an overall objective to rationalize the affective property of apparel. To achieve this overall objective, the first step is to have a language (a set of words in this case) to describe the customer’s need in the affective attribute or property of apparel into a technical specification. In the work reported in this paper, this language (simply, a set of words) has been developed by the application of a proposed data mining procedure with a proprietary tool. A preliminary experiment was performed to validate this language – how to accurately capture the voice of customer in the aspect of affects in this case. There are two contributions out of this work: (1) finding a set of words that describe the affective property of apparel to capture the voice of customer in the aspect of affect, which is a foundation for the computer-based affective design of apparel; and (2) formulation of a new data mining process for searching affective words from the internet, which has a generalized implication to affective design in other domains of products, such as furniture.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".