Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Siddique, Mohammad Mustafa; Kumar, Sandeep Title A name given to the resource Multilingual Sentiment Analytics for India's NEP 2020 Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Conference Proceedings - 2025 IEEE 4th International Conference on Data, Decision and Systems, ICDDS 2025;pp.232-235 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDDS67737.2025.11344683" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDDS67737.2025.11344683</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033363748?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033363748?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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&lt;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&lt;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. Subject The topic of the resource Cross linguistic NLP; Education policy analytics; hybrid annotation pipeline; Multilingual sentiment analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155479-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online