Sentiment analysis of Amazon product 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
The rapid development of online shopping sites has pushed people's shopping to a new way. Online shopping not only provides convenience to people but also "suggestions." Moreover, there are always many reviews from previous consumers on shopping websites, helping people know more about the product and make decisions. This paper represents the sentiment analysis of Amazon reviews using three models: Random Forest, Naive Bayes, and SVM. These models are trained with token counts, and term frequency-inverse document frequency (TF-IDF) features to make better comparisons. Classification performances are evaluated by precision, recall, and F-1 scores, and exploration is implemented into the dataset providing information about Amazon reviews. The results show that Random Forest and SVM models perform well on positive-labeled data but provide suboptimal results on negative-labeled and neutral-labeled data. Overall, Naive Bayes has the best performance for all three classifications. However, classifications might be biased during the analysis. Thus, more improvements are expected in future research about this topic to obtain more accurate and ideal results, and more machine learning models are supposed to be implemented.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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