ABSA-Driven Recommendation System for Domain-Specific Decision-Making
Bibliographic record
Abstract
In democracy like India, public opinion is a significant factor in the formulation of government policies and measure. Understanding people's feelings and concerns about certain aspects of government behaviour is important for governance and policy development. Traditional sentiment analysis methods often fail to capture the crucial sentiments and emotions expressed by the public. ABSA deals with three main functions: aspect extraction, category detection and sentiment classification. By exploring the details of each function, we aim to explain the process of understanding the public opinion of ABSA, its importance and feasibility for the government of India. We propose a model using BERT+CRF for aspect extraction, RoBERTa with attention for aspect category detection, and DistilBERT+GRU with attention for sentiment classification, ensuring precise and context-aware analysis. The proposed a model obtain 0.914 accuracy, 0.937 precision, 0.935 recall, 0.915 f1 score, and 0.874 AUC-ROC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".