Arabic Sentiment Analysis for ChatGPT Using Machine Learning Classification Algorithms: A Hyperparameter Optimization Technique
Why this work is in the frame
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Bibliographic record
Abstract
In the realm of ChatGPT's language capabilities, exploring Arabic Sentiment Analysis emerges as a crucial research focus. This study centers on ChatGPT, a popular machine learning model engaging in dialogues with users, garnering attention for its exceptional performance and widespread impact, particularly in the Arab world. The objective is to assess people's opinions about ChatGPT, categorizing them as positive or negative. Despite abundant research in English, there is a notable gap in Arabic studies. We assembled a dataset from X (formerly known as Twitter), comprising 2,247 tweets, classified by Arabic language specialists. Employing various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB), we implemented hyperparameter optimization techniques such as Bayesian optimization, Grid Search, and random search to select the best hyperparameters that contribute to achieving the best performance. Through training and testing, performance enhancements were observed with optimization algorithms. SVM exhibited superior performance, achieving 90% accuracy, 88% precision, 95% recall, and 91% F1 score with Grid Search. These findings contribute valuable insights into ChatGPT's impact in the Arab world, offering a comprehensive understanding of sentiment analysis through machine learning methodologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it