A Suitable Technique for Enhancing Arabic-Language Consumer Sentiment Analysis Using Natural Language Processing and Stacking Machine Learning Model
Bibliographic record
Abstract
When deciding on a product, sentiments expressed on social media or online reviews are important information sources.Positive and negative feedback from customers posted on social media platforms could substantially impact a business's bottom line.As a result, the development of effective and efficient approaches for classifying emotion has emerged as one of the most pressing concerns for businesses.Applying machine learning is widely regarded as one of the most effective and beneficial ways.This work will investigate how well Machine Learning (ML) techniques can comprehend Arabic sentiments.The Term Frequency-Inverse Document Frequency algorithm (TF-IDF) was used to extract the dataset's characteristics.As a consequence of this, the algorithms known as Random Forest (RF), Decision Tree (DT), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), support vector machine (SVM), Quadratic Discriminant Analysis (QDA), logistic regression (LR), Gradient Boosting Regression Trees (GBRT), and Stochastic Gradient Descent (SGD) Classifier are used in the process of sentiment analysis (SA).To sum everything up, a stacked machine-learning model was developed.Compared to existing machine learning simple classifiers, our stacked model with 10-fold cross-validation shows a higher accuracy, precision, Cohen's Kappa, recall, and F1-score in the three different Arabic datasets used, which are the Hotel Arabic-Reviews Dataset (A), the Books Reviews in Arabic Dataset (B), and the Arabic Reviews dataset (C).
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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 | 0.001 | 0.001 |
| 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".