Travel Vlog Reviews: Support Vector Machine Performance in Sentiment Classification
Bibliographic record
Abstract
This research investigates the combination of the Support Vector Machine algorithm with the Synthetic Minority Over-sampling Technique to improve classification performance in sentiment analysis, especially in handling imbalanced datasets.Employing a dataset comprising 1,928 text entries, the research highlights SVM's challenges in managing imbalanced data, where a predisposition toward the majority class leads to less-than-optimal classification results.Through the application of SMOTE, synthetic samples were generated to balance the minority class, resulting in notable performance improvements, including an accuracy of 83.12%, a precision of 75.76%, a recall of 97.53%, and an Area Under the Curve (AUC) score of 0.978.These outcomes emphasize the effectiveness of integrating SVM and SMOTE to balance class distributions and enhance the model's capacity to distinguish between positive and negative sentiments.The findings underscore the importance of strategic model optimization to achieve balanced results and contribute to advancements in sentiment analysis methodologies.
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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 | 0.001 | 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 | 0.000 | 0.003 |
| 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 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".