Sentiment analysis of Saudi e-commerce using naïve bayes algorithm and support vector machine
Bibliographic record
Abstract
The Covid-19 pandemic which has spread across all countries, including Saudi Arabia, has caused the government to create limited curfew policies in the country that affected the economy. This policy has given rise to a new trend in society, namely the habit of shopping online. The trend of purchasing online via e-commerce increases. However, people's opinions and attitudes towards this trend vary. Therefore, this research was conducted with the aim of determining the subjectivity of public opinion or sentiment on the e-commerce activities using probability and statistical approaches, i.e.: the Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers. Three experimental scenarios of dataset splitting for training and testing; 90%:10%; 80%:20%; and 70%:30%. The comparison of accuracy values was carried out using an automatic labeling method. Experimental results show that the 70%:30% split scenario provides the best result, with 89% of accuracy, 99.7% of Precision, 88% of Recall and 93.5% of F1-score for the SVM classifier.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".