A two-level deep learning approach for emotion recognition in Arabic news headlines
Bibliographic record
Abstract
Going online has created more opportunities for newspapers to present breaking news in a timely manner. Concentrating in spreading more bad news increases the feeling of danger and depression in the society. Some authors believe on tendency of some media to be focused on sharing the bad events in life rather than the good ones because of the impact and the attraction over the audience is more significant. Sentiment analysis work has not been recognized, proposed, or documented on Arabic news because of the challenges that Arabic raises as a language including the different Arabs' dialects and its complex grammatical structure. With the emerging flow of news that cause a global panic and anxiety worldwide, this study focuses on the serious need to identify what effective role could machine learning classifiers have in the early detection process of the psychology impact on the readers by the daily news headlines. In this work, a dataset of news headlines were gathered from top popular online Arabic news sites and were annotated to seven emotional categories: anger, disgust, fear, happiness, neutral, sadness, and surprise. A convolutional neural network-based two-level approach was proposed for sentiment classification. The performance of the proposed approach was compared to six machine learning classifiers (zeroR, k-nearest neighbor, decision trees, naïve Bayes, random forest and Support vector machine) and showed better accuracy, precision, and recall.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".