Support Vector Machine for Sentiment Analysis of PT. Paragon Technology and Innovation (Case Study of Brand Make Over and Emina Product Users on Female Daily Page – Beauty Review)
Bibliographic record
Abstract
Support Vector Machine (SVM) is one of the classification models in Supervised Learning that is commonly used to classify user sentiment towards certain products or services. Facial care products are widely used by all circles, especially Gen-Z. The aim of this study is to obtain sentiment from the specified product reviews and apply the SVM method to predict sentiment classification. The products analyzed consisted of two Emina products, namely sunscreen and face wash and three Make Over products, namely eyebrow pencil, blush, and lipstick. The sentiment results of the five products showed that Make Over blush on products received the most positive sentiment at 98%, while Emina Sunscreen products received the least, with only at 66%. The SVM model in this study showed a good performance in making predictions with accuracy on all five products above 80%. In addition, the level of accuracy of the SVM model in classifying that the data is included in the positive class on the five products is also good because it has a recall value of > 75%. The results of this study can be used as an evaluation for PT. Paragon Technology and Innovation to continuously improve product quality and SVM models can be used to predict classification in other studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".