Application of Machine Learning to Mining Customer 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
Online customer reviews are important sources of information influencing consumers’ attitudes towards products and brands. Businesses also use them to gain deeper insights into consumers’ perceptions, attitudes, and behaviors. This study uses machine learning (ML) and participant-based attributes of reviewers to classify them into distinct segments. The segments are then labelled and used to build a predictive model of customer behavior, which can help companies quickly profile customers and develop appropriate marketing strategies. The results show that our machine learning approach coupled with participation-based attributes generated unique clusters that are consistent with prior classification of online audiences. The personas-based clusters can help marketers make better use of reviewers in marketing campaigns by engaging them differently based on their interests and status in the online community. This study opens the door for further research using ML with larger and different review sites coupled with additional psychological, social, and economic variables.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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