Multilingual Sentiment Analytics for India's NEP 2020
subject: Cross linguistic NLP; Education policy analytics; hybrid annotation pipeline; Multilingual sentiment analysis
description: This study presents a multilingual sentiment analysis framework for evaluating public sentiments on India's National Education Policy (NEP) 2020. The authors developed a dataset related to NEP 2020 using web scraping from open sources. The curated dataset comprises 50,000 social media posts (English: 30,000, Hindi: 12,000, Tamil: 8,000) processed through a confidence-gated hybrid annotation pipeline. Sentiment labels were created using Transformer models (BERT, mBERT, XLMR) and validated by native-speaker with F1-scores of 87.6%, 81.2% and 78.0% for English, Hindi and Tamil respectively: outperforming baselines (SVM, Naive Bayes, BiLSTM) by 12-18% (p<0.001). We use computational efficiency measures to illustrate that training takes 3.2-5.3 hours and inference lasts between 118 and 187 posts per second. Topic modeling revealed sentiment divergences: positive for linguistic inclusivity and teacher training, negative for affordability and infrastructure. Cross-linguistic analysis showed English-Hindi convergence (similarity: 0.61) versus Tamil divergence (0.46), reflecting regional priorities. Tamil emphasized linguistic identity while English prioritized implementation critiques. Quantitative policy impact analysis shows very strong correlation (r=0.68, p<0.01) between regional sentiment scores and state adoption rates. This open-sourced contribution is filling the crucial gap of inclusive policy analytics in multilingual society informing evidence-based policy. 2025 IEEE.
creator: Siddique, Mohammad Mustafa; Kumar, Sandeep
source: Conference Proceedings - 2025 IEEE 4th International Conference on Data, Decision and Systems, ICDDS 2025;pp.232-235
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833155479-8;
language: English
type: Conference paper
coverage: Siddique M.M., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Kumar S., Data Science Christ University, Department of AI, Ml, Bengaluru, India