Evaluating Sentiments in Social Media Comments on Tax Transformation in India using Deep Learning
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Bibliographic record
Abstract
The Goods and Services Tax (GST) was implemented by the government of India to have one tax for the entire country. Millions of taxpayers commented on their experience of the GST e-governance system on the Twitter platform, which was collated for the study from June 2017 to May 2020. This paper proposes a comprehensive approach for finding the variation of attention weights for key words (related to broad categories belonging to GST) present in the tweets over different quarters of the three-year period along with month-wise sentiment prediction for every quarter using Bi-directional LSTM model with attention. The contribution of key words, whose attention weights exceed two sigma thresholds, towards the net positive and negative sentiments of tweets is found to be significant in the study.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it