DZ-OPINION: Algerian Dialect Opinion Analysis Model with Deep Learning Techniques
Bibliographic record
Abstract
Today, many sources of unstructured information such as social networks and blogs are more or less freely available on the web, and their volume is constantly growing, which constitutes a free gold mine for collecting public opinion. Opinion plays a crucial role because it can influence the decision-making process. Sentiment or opinion analysis is a discipline that can be used to meet decision-making needs, provide feedback to new product launches and marketing campaigns, and protect a company’s reputation, especially in social networking environments with massive data by exploiting textual data generated by users. In contrast to the techniques used, we have used and adapted in this study deep learning techniques: CNN and LSTM to identify their potential in this area and apply it to a corpus of Arabic data and in particular in Algerian dialect collected from social networks (50572 Facebook comments). We obtained promising results with an 85% f-measure. This represents a good start for an opinion analysis on the Algerian Dialect.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".