An NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features
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
This study focuses on predicting the popularity of a product based on its overall rating score. Unlike previous studies that focus on predicting the review rating based on sentiment analysis and the polarity of the reviews, in this research, the effect of product features in determining its popularity is directly measured and analyzed. To this end, a methodology consisting of three phases is considered. Phase 1 predicts the overall rating by feeding the general product features, extracted from the online product information available on Amazon webpages to three different deep learning (DL) models: deep feedforward neural network (DFFNN), probabilistic neural network (PNN), and radial basis function neural network (RBFNN). Phase 2 identifies other features that customers care about the most by applying the named entity recognition (NER) algorithm to the customer online reviews. Finally, Phase 3 feeds the combination of the general and custom features to the same DL models to predict the overall rating score of the product. The experimental results on a dataset of laptop products indicate an impressive performance of the proposed approach, which is mainly attributed to including custom product features in the inputs of the DL algorithm. More precisely, the proposed model could achieve the highest accuracy score of 84.01%, 84.68% for recall, 87.63% for precision, and 84.06% for F1 score. Applying this procedure could help businesses identify the specific areas of strengths and weaknesses of their products or services from the perspective of their customers, allowing them to thrive in today’s competitive markets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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