Mining affective words to capture customer’s affective response to apparel products
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
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.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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