An Innovative Arabic Word Embedding Representation for Enhanced Sentiment Analysis
Bibliographic record
Abstract
Despite the plethora of data generated on Arabic social media, research dedicated to this language remains comparatively scarce.Sentiment analysis, an extensively studied field in various languages, has seen limited development in Arabic.Existing approaches to Arabic sentiment analysis primarily employ machine learning, wherein word vector representations serve as features for model training.A significant challenge encountered in this approach is the substantial volume and sparsity of the matrix representation, attributable to the extensive vocabulary of the Arabic language.This paper proposes a novel word embedding that amalgamates the Bag of Roots (BoR) technique with Global Vector distributional representations (GloVe).This innovation is inspired by the characteristic of the Arabic language, where it is rare to find two or more words sharing the same root but conveying different sentiments.The impact of this innovative word embedding technique is highlighted through an evaluation using sentiment analysis.This involves the implementation of conventional classifiers, specifically Support Vector Machines (SVM) and Logistic Regression (LR).The results obtained demonstrate promising precision, recall, and F1-score metrics.Additionally, a significant reduction in processing time is observed when compared to other approaches referenced in literature.Thus, this paper contributes to the advancement of Arabic sentiment analysis, offering a potential pathway to overcoming the challenges associated with the large vocabulary and complex structure of the Arabic language.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".