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Original Article
Tai Ahom Sentiment Analysis System Using Lexicon-Based and Naive Bayes Approaches
Racktutpal Khataniar1
Dr. Dhrubajyoti Baruah2
1 2 Department of Computer Application, Jorhat Engineering College, Assam, India.
Published Online: May-August 2026
Pages: 761-767
Cite this article
No DOIReferences
1. Baruah, D., & Boruah, A. (2025). Analysis of Assamese Backed English Generated Sentiment: AABEG. Indian Journal of Computer Science
and Technology, 4(3), 203–211.
2. Hutto, C., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the 8th
International AAAI Conference on Weblogs and Social Media (ICWSM).
3. Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of Naive Bayes text classifiers. Proceedings of the20th International Conference on Machine Learning (ICML-03), 616–623.
4. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12,
2825–2830.
5. Hazarika, G., & Sharoff, S. (2019). Building NLP resources for low-resource languages of Northeast India: Challenges and strategies. Language
Resources and Evaluation, 53(4), 745–768.
6. SEAlang Library. Tai Ahom Dictionary. Retrieved from https://sealang.net/ahom
7. Unicode Consortium. (2023). The Unicode Standard, Version 15.0, Tai Ahom Block U+11700–U+1173F.
8. Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media.
and Technology, 4(3), 203–211.
2. Hutto, C., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the 8th
International AAAI Conference on Weblogs and Social Media (ICWSM).
3. Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of Naive Bayes text classifiers. Proceedings of the20th International Conference on Machine Learning (ICML-03), 616–623.
4. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12,
2825–2830.
5. Hazarika, G., & Sharoff, S. (2019). Building NLP resources for low-resource languages of Northeast India: Challenges and strategies. Language
Resources and Evaluation, 53(4), 745–768.
6. SEAlang Library. Tai Ahom Dictionary. Retrieved from https://sealang.net/ahom
7. Unicode Consortium. (2023). The Unicode Standard, Version 15.0, Tai Ahom Block U+11700–U+1173F.
8. Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media.
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