Application of Sentiment Analysis on Product Reviews of the Binjai Langkat Buket Shop to Improve Customer Service using the Naive Bayes Method
Bibliographic record
Abstract
In the digital era, customer reviews are spread across various social media and e-commerce platforms, posing a challenge for micro-businesses to evaluate sentiment efficiently. This study aims to develop an automated sentiment analysis system for product reviews of Toko Buket Binjai Langkat using the Term Frequency-Inverse Document Frequency (TF-IDF) method for feature extraction and the Naive Bayes algorithm for positive, negative, and neutral sentiment classification. Data were collected through web scraping techniques and processed with preprocessing stages such as case folding, stopword removal, and stemming. The model was trained and tested with a 70:30 data split and evaluated using accuracy, precision, recall, and F1-score metrics. The test results showed that the model's accuracy reached 91%, with the best performance on positive sentiment (precision 0.89, recall 1.00, F1-score 0.94), but there were limitations in detecting negative sentiment (recall 0.42) due to data imbalance. This study provides practical contributions for micro-businesses in understanding customer opinions and formulating data-driven service improvement strategies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".