Subject Detection of Algerian Posts for Opinion Analysis
Bibliographic record
Abstract
Nowadays, opinion analysis and text classification become interesting tasks in social media studies.Moreover, keyword extraction technology is important research topic and the basis of building corpora, information retrieval, text analysis and text classification...etc.Most studies carry out on the binary problem classification which has historically received more attention in the machine learning community compared to multi-class classification problem.They are often done on academic languages, leaving aside the dialects despite of their increasing use in the social networks.There is very little effort that has been dedicated in the Arabic language especially its dialects such as Algerian dialect, intended for the analysis of opinions.In this work, we aim to realize an innovative and original approach for multi-class classification task and subject detection in the field of marketing.These classes are considered as subjects of the Algerian posts.We collected dataset from Facebook and annotated them with 11 labels.We applied TF-IDF word embedding method to vectorise and extract keywords by giving a weight for each token.In the final stage, our model was trained using input vectors.We applied a deep neural network on our annotated dataset.We achieved a precision of 83%.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".