A Deep Convolutional Neural Network Model to Predict Consumer Recommendations using Online Reviews
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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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