Application of Sentiment Analysis on Product Reviews of the Binjai Langkat Buket Shop to Improve Customer Service using the Naive Bayes Method
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
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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