Identification of the to-be-improved product features based on online reviews for product redesign
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
Acquisition of customer needs usually serves as the basis for the identification of to-be-improved features for the product redesign process. However, the customer's true needs tend to be non-obvious and are difficult to extract from the data source like interviews or market survey. In the era of Big Data, with the advances in e-commerce, the customer's online review has become one of the most important data source to reveal the insight of customer's preference. In this paper, an online-review-based approach is introduced to identify the to-be-improved product features. The product features and corresponding opinions are extracted and reduced based on the semantic similarity. A structured preference model based on the semantic orientation analysis is constructed. A redesign index is subsequently introduced to measure the priority of redesign for each feature, and a target feature selection model is created to identify the to-be-improved features from candidate features considering engineering cost, redesign lead time and technical risk. A case study for smartphones is developed to demonstrate the effectiveness of the developed approach. In the future study, the online reviews may be combined with the traditional survey data to provide a more effective and reliable identification on the to-be-improved product features.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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