A Deep Convolutional Neural Network Model to Predict Consumer Recommendations using Online Reviews
Bibliographic record
Abstract
Detecting consumer perception using online reviews is challenging. Artificial Intelligence (AI) techniques have the potential to understand human perception. Sensing human psychology is essential for business growth and selecting products or services in emerging markets. This paper proposes a Deep Convolutional Neural Network (DCNN) model to predict recommendations using consumer-generated online reviews. It reinforces the capability of Deep Learning (DL) models to consider two aspects of online reviews, qualitative and quantitative, and their combinations to predictive recommendations. We have collected online reviews of airline passengers from Skytrax with the objective. We have implemented various Natural Language Processing (NLP) techniques to process the qualitative contents of online reviews. Furthermore, pre-processed data with ratings on different service aspects feeds the proposed DCNN model. To validate the performance of the proposed model, we have evaluated different performance evaluation parameters such as precision, F-score, recall, and accuracy. The experimental analysis demonstrates that the DCNN model outperforms traditional Machine Learning (ML) models for predictive recommendations. Our research indicates the power of online reviews in understanding consumer intentions for emerging market growth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".