A HYBRID METHOD USING LEXICON-BASED APPROACH AND NAIVE BAYES CLASSIFIER FOR ARABIC OPINION QUESTION ANSWERING
Bibliographic record
Abstract
Opinion Question Answering (Opinion QA) is the task of enabling users to explore others opinions toward a particular service of product in order to make decisions.Arabic Opinion QA is more challenging due to its complex morphology compared to other languages and has many varieties dialects.On the other hand, there are insignificant research efforts and resources available that focus on Opinion QA in Arabic.This study aims to address the difficulties of Arabic opinion QA by proposing a hybrid method of lexicon-based approach and classification using Naïve Bayes classifier.The proposed method contains pre-processing phases such as, transformation, normalization and tokenization and exploiting auxiliary information (thesaurus).The lexiconbased approach is executed by replacing some words with its synonyms using the domain dictionary.The classification task is performed by Naïve Bayes classifier to classify the opinions based on the positive or negative sentiment polarity.The proposed method has been evaluated using the common information retrieval metrics i.e., Precision, Recall and F-measure.For comparison, three classifiers have been applied which are Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).The experimental results have demonstrated that NB outperforms SVM and KNN by achieving 91% accuracy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".